+
+ Parameters
+ ----------
+ folder_path : str
+ Absolute or relative path whose final component encodes the layout.
+
+ Returns
+ -------
+ layout_key : tuple[int, int, bool] or None
+ ``(rows, cols, is_cabrera)``. ``None`` if the name does not match.
+ calibration : bool or None
+ ``True`` when a rhythm (calibration) strip is present, ``None`` on
+ parse failure.
+ """
+ base_name = folder_path.split('/')[-1]
+ match = re.search(r'ecg_signals_(\d+)x(\d+)_(None|Rythm)_(Cabrera|Normal)', base_name)
+ if match:
+ rows = int(match.group(1))
+ cols = int(match.group(2))
+ calibration = (match.group(3) == 'Rythm')
+ cabrera_flag = (match.group(4) == 'Cabrera')
+ layout_key = (rows, cols, cabrera_flag)
+ return layout_key, calibration
+ else:
+ return None, None
+
+
+class ECGImage:
+ """End-to-end digitizer for a single 12-lead ECG image.
+
+ The class encapsulates every processing stage โ from raw pixel
+ loading to per-lead voltage time-series export. All intermediate
+ results (masks, bounding boxes, calibration constants, signal grid)
+ are stored as instance attributes so that individual stages can be
+ inspected or visualised after the pipeline runs.
+
+ Typical usage
+ -------------
+ >>> ecg = ECGImage(box_model, seg_model, name_model, pulse_model, "ecg.png")
+ >>> ecg.run_full_pipeline()
+ >>> ecg.save_signals_as_csv("record_001", directory="./output")
+
+ Parameters
+ ----------
+ box_model : ultralytics.YOLO
+ Pre-loaded YOLO model that detects lead bounding boxes.
+ segmentation_model : str
+ Path to the YOLO segmentation model checkpoint. Loaded
+ internally by ``patched_yolo_infer.MakeCropsDetectThem`` so
+ that it can operate on image patches.
+ lead_name_model : ultralytics.YOLO
+ Pre-loaded YOLO model that classifies lead name labels
+ (I, II, III, aVR, aVL, aVF, V1โV6).
+ pulse_model : ultralytics.YOLO
+ Pre-loaded YOLO model that detects calibration pulse boxes.
+ image_path : str
+ Path to the ECG image (.png, .jpg, or .jpeg).
+ wfdb_path : str, optional
+ Path to a WFDB record (without extension) used for ground-truth
+ comparison in :meth:`calculate_metrics_ptb`. Not required for
+ normal digitization.
+
+ Attributes set by the pipeline
+ --------------------------------
+ image : np.ndarray
+ Loaded and padded grayscale image.
+ processed_image : np.ndarray
+ Shadow-removed, blurred version used for all model inference.
+ lead_segmentation : list
+ ``CombineDetections`` objects from the three segmentation passes.
+ mask_image : np.ndarray
+ Binary mask (H ร W, uint8) combining all segmented lead polygons.
+ row_centers : np.ndarray
+ Y-pixel positions of detected lead row centres.
+ roi : tuple[float, float]
+ ``(min_y, max_y)`` bounding the active ECG area.
+ lead_bboxes : list[list[float]]
+ ``[x1, y1, x2, y2]`` boxes from ``box_model``.
+ lead_name_bboxes : list[dict]
+ ``{'bbox': [...], 'class_name': str}`` from ``lead_name_model``.
+ reference_pulses : list[dict]
+ ``{'bbox': [...], 'image': np.ndarray}`` from ``pulse_model``.
+ volt_per_pixel : float
+ Calibration constant: millivolts per pixel (vertical axis).
+ time_per_pixel : float
+ Calibration constant: seconds per pixel (horizontal axis).
+ is_cabrera : bool
+ Whether the ECG uses Cabrera lead ordering.
+ has_calibration_pulse : bool
+ Whether a rhythm/calibration strip row is present.
+ layout : list[list[str]]
+ 2-D grid of lead names matching the detected row/column layout.
+ grid : list[list[dict]]
+ Mask grid โ each cell contains ``'lead'`` (str) and ``'signal'``
+ (np.ndarray of the binary mask slice).
+ signal_grid : list[list[dict]]
+ After :meth:`extract_signals`: cells additionally contain
+ ``'signal'`` as a list of float mV values.
+ """
+
+ def __init__(self, box_model, segmentation_model, lead_name_model, pulse_model,
+ image_path, wfdb_path=""):
+ self.image_path = image_path # stored for retry in run_full_pipeline
+ self.load_image(image_path)
+ self.wfdb_path = wfdb_path
+
+ self.box_model = box_model
+ self.segmentation_model = segmentation_model
+ self.lead_name_model = lead_name_model
+ self.pulse_model = pulse_model
+
+ self.is_cabrera = None
+ self.has_calibration_pulse = None
+
+ self.standard_layouts = {
+ # Standard layouts
+ (12, 1, False): [['I'], ['II'], ['III'], ['aVR'], ['aVL'], ['aVF'],
+ ['V1'], ['V2'], ['V3'], ['V4'], ['V5'], ['V6']],
+ (6, 2, False): [['I', 'V1'], ['II', 'V2'], ['III', 'V3'],
+ ['aVR', 'V4'], ['aVL', 'V5'], ['aVF', 'V6']],
+ (4, 3, False): [['I', 'II', 'III'], ['aVR', 'aVL', 'aVF'],
+ ['V1', 'V2', 'V3'], ['V4', 'V5', 'V6']],
+ (3, 4, False): [['I', 'aVR', 'V1', 'V4'], ['II', 'aVL', 'V2', 'V5'],
+ ['III', 'aVF', 'V3', 'V6']],
+ # Cabrera layouts
+ (12, 1, True): [['aVL'], ['I'], ['aVR'], ['II'], ['aVF'], ['III'],
+ ['V1'], ['V2'], ['V3'], ['V4'], ['V5'], ['V6']],
+ (6, 2, True): [['aVL', 'V1'], ['I', 'V2'], ['aVR', 'V3'],
+ ['II', 'V4'], ['aVF', 'V5'], ['III', 'V6']],
+ (4, 3, True): [['aVL', 'I', 'aVR'], ['II', 'aVF', 'III'],
+ ['V1', 'V2', 'V3'], ['V4', 'V5', 'V6']],
+ (3, 4, True): [['aVL', 'II', 'V1', 'V4'], ['I', 'aVF', 'V2', 'V5'],
+ ['aVR', 'III', 'V3', 'V6']],
+ }
+
+ # ------------------------------------------------------------------
+ # Image loading & preprocessing
+ # ------------------------------------------------------------------
+
+ def load_image(self, path, target_size=2100):
+ """Load, resize, and pad an ECG image.
+
+ The image is scaled so that its height equals *target_size* (cubic
+ interpolation) and then padded with a 20-pixel white border on all
+ sides. The result is stored in ``self.image``.
+
+ Parameters
+ ----------
+ path : str
+ Path to the image file.
+ target_size : int, optional
+ Target image height in pixels (default ``2100``).
+
+ Raises
+ ------
+ FileNotFoundError
+ If OpenCV cannot open the file at *path*.
+ """
+ img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+ if img is None:
+ raise FileNotFoundError(f"Cannot read image: {path}")
+ resample_factor = target_size / img.shape[0]
+ img = cv2.resize(img,
+ (int(img.shape[1] * resample_factor),
+ int(img.shape[0] * resample_factor)),
+ interpolation=cv2.INTER_CUBIC)
+ self.image = cv2.copyMakeBorder(img, 20, 20, 20, 20,
+ cv2.BORDER_CONSTANT, value=[255, 255, 255])
+
+ def preprocess_image(self):
+ """Apply shadow removal and Gaussian smoothing to ``self.image``.
+
+ The result is stored in ``self.processed_image`` and is used as
+ input for all subsequent YOLO inference calls.
+ """
+ rem = shadow_removal(self.image)
+ self.processed_image = cv2.GaussianBlur(rem, (3, 3), 0)
+
+ # ------------------------------------------------------------------
+ # Segmentation
+ # ------------------------------------------------------------------
+
+ def segment_leads(self):
+ """Run patched YOLO instance segmentation at three crop scales.
+
+ The segmentation model is applied to overlapping image patches at
+ crop factors of 4, 4.5, and 5 (relative to image height).
+ Detections from each pass are combined with NMS
+ (``nms_threshold=0.5``) and stored in ``self.lead_segmentation``
+ as a list of three ``CombineDetections`` objects.
+ """
+ segmentations = []
+ for shape in [4, 4.5, 5]:
+ element_crops = MakeCropsDetectThem(
+ image=cv2.cvtColor(self.processed_image, cv2.COLOR_GRAY2BGR),
+ model_path=self.segmentation_model,
+ segment=True,
+ show_crops=False,
+ shape_x=int(self.processed_image.shape[0] // shape),
+ shape_y=int(self.processed_image.shape[0] // shape),
+ overlap_x=50,
+ overlap_y=50,
+ conf=0.8,
+ iou=0.7,
+ classes_list=[0],
+ )
+ segmentations.append(CombineDetections(element_crops, nms_threshold=0.5))
+ self.lead_segmentation = segmentations
+
+ def make_segmentation_mask(self):
+ """Rasterise all segmented polygons into a single binary mask.
+
+ Polygons from all three segmentation passes in
+ ``self.lead_segmentation`` are filled and merged into a combined
+ mask. A morphological opening (5 ร 5 kernel) removes small
+ spurious regions. The result is stored in ``self.mask_image``
+ (uint8, values 0 or 255).
+ """
+ height, width = self.image.shape[:2]
+ combined_mask = np.zeros((height, width), dtype=np.uint8)
+
+ for segmentation in self.lead_segmentation:
+ for poly in segmentation.filtered_polygons:
+ pts = np.array(poly, dtype=np.int32)
+ if pts.ndim == 2:
+ pts = [pts]
+ cv2.fillPoly(combined_mask, pts, color=255)
+
+ kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+ self.mask_image = cv2.morphologyEx(combined_mask, cv2.MORPH_OPEN, kernel)
+
+ # ------------------------------------------------------------------
+ # Row detection
+ # ------------------------------------------------------------------
+
+ def find_row_centers(self):
+ """Detect the vertical centre of each ECG lead row.
+
+ Projects the binary mask onto the vertical axis (row sums) and
+ applies ``scipy.signal.find_peaks`` twice: once to identify all
+ peaks, and a second time with an adaptive minimum distance
+ (โ
ร mean inter-peak spacing) to consolidate merged rows.
+
+ Sets
+ ----
+ self.row_centers : np.ndarray
+ Y-pixel indices of each row centre.
+ self.first_peak_start : int
+ Y-pixel where the topmost row's signal begins.
+ self.last_peak_end : int
+ Y-pixel where the bottommost row's signal ends.
+ """
+ image_height, image_width = self.mask_image.shape[:2]
+ proj = np.sum(self.mask_image, 1)
+
+ height = (image_width // 10) * 255
+ distance = image_height // 30
+
+ peaks, _ = find_peaks(proj, height=height, distance=distance)
+ row_centers, _ = find_peaks(
+ proj, height=height,
+ distance=int(np.mean(np.diff(peaks)) * (2 / 3))
+ )
+ self.row_centers = row_centers
+
+ if len(peaks) > 0:
+ first_peak = peaks[0]
+ start_index = first_peak
+ zero_gap = 0
+ for i in range(first_peak - 1, -1, -1):
+ if proj[i] == 0:
+ zero_gap += 1
+ if zero_gap > 2:
+ break
+ else:
+ zero_gap = 0
+ start_index = i
+ self.first_peak_start = start_index
+
+ last_peak = peaks[-1]
+ end_index = last_peak
+ zero_gap = 0
+ for i in range(last_peak + 1, image_height):
+ if proj[i] == 0:
+ zero_gap += 1
+ if zero_gap > 2:
+ break
+ else:
+ zero_gap = 0
+ end_index = i
+ self.last_peak_end = end_index
+
+ def get_roi(self):
+ """Compute the vertical region-of-interest (ROI) spanning all lead rows.
+
+ Extends โ
of the mean row spacing above the first row centre and
+ below the last. Stores the result as ``self.roi = (min_y, max_y)``.
+ """
+ spacing = 2 / 3 * np.mean(np.diff(self.row_centers))
+ min_y = max(0, self.row_centers[0] - spacing)
+ max_y = min(self.image.shape[0], self.row_centers[-1] + spacing)
+ self.roi = (min_y, max_y)
+
+ # ------------------------------------------------------------------
+ # YOLO inference helpers โ GPU-safe wrappers
+ # ------------------------------------------------------------------
+
+ def _predict_safe(self, model, image_bgr, **kwargs):
+ """Run a YOLO model prediction in a thread-safe manner.
+
+ On CUDA: wraps the call in a dedicated ``torch.cuda.Stream`` and
+ synchronises before returning, preventing contention on the
+ default stream when multiple threads call this simultaneously.
+ On CPU: wraps with ``torch.no_grad()`` only.
+
+ Parameters
+ ----------
+ model : ultralytics.YOLO
+ The detection/classification model to run.
+ image_bgr : np.ndarray
+ BGR image array as expected by Ultralytics.
+ **kwargs
+ Forwarded to ``model.predict()`` (e.g. ``conf``, ``iou``).
+
+ Returns
+ -------
+ list
+ Ultralytics ``Results`` objects.
+ """
+ if torch.cuda.is_available():
+ stream = torch.cuda.Stream()
+ with torch.no_grad():
+ with torch.cuda.stream(stream):
+ results = model.predict(image_bgr, verbose=False, **kwargs)
+ stream.synchronize()
+ else:
+ with torch.no_grad():
+ results = model.predict(image_bgr, verbose=False, **kwargs)
+ return results
+
+ # ------------------------------------------------------------------
+ # Timing helper
+ # ------------------------------------------------------------------
+
+ @staticmethod
+ def _timed(label, fn, *args, **kwargs):
+ """Call *fn* with *args*/*kwargs*, print its wall-clock duration, and return its result.
+
+ Parameters
+ ----------
+ label : str
+ Human-readable stage name printed alongside the elapsed time.
+ fn : callable
+ The stage function to time.
+ *args, **kwargs
+ Forwarded to *fn*.
+
+ Returns
+ -------
+ Any
+ Whatever *fn* returns.
+ """
+ t = time.time()
+ result = fn(*args, **kwargs)
+ print(f" โฑ {label}: {time.time() - t:.1f}s", flush=True)
+ return result
+
+ # ------------------------------------------------------------------
+ # Box & name extraction
+ # ------------------------------------------------------------------
+
+ def extract_lead_boxes(self):
+ """Detect lead bounding boxes with ``box_model`` and filter to the ROI.
+
+ Runs ``_predict_safe`` on the preprocessed BGR image at confidence
+ 0.8, then discards boxes whose ``y1``/``y2`` coordinates fall
+ outside ``self.roi``. Sets ``self.lead_bboxes`` as a list of
+ ``[x1, y1, x2, y2]`` float lists.
+ """
+ image_bgr = cv2.cvtColor(self.processed_image, cv2.COLOR_GRAY2BGR)
+ results = self._predict_safe(self.box_model, image_bgr, conf=0.8)
+
+ min_y, max_y = self.roi
+ lead_boxes = []
+ for r in results:
+ for box in r.boxes:
+ x1, y1, x2, y2 = box.xyxy.cpu().numpy()[0].tolist()
+ if y1 >= min_y and y2 <= max_y:
+ lead_boxes.append([x1, y1, x2, y2])
+ self.lead_bboxes = lead_boxes
+
+ def extract_lead_name_boxes(self):
+ """Detect and classify lead name labels with ``lead_name_model``.
+
+ Runs at confidence 0.8, filters to the ROI, and stores results in
+ ``self.lead_name_bboxes`` as a list of
+ ``{'bbox': [x1, y1, x2, y2], 'class_name': str}`` dicts.
+ """
+ image_bgr = cv2.cvtColor(self.processed_image, cv2.COLOR_GRAY2BGR)
+ results = self._predict_safe(self.lead_name_model, image_bgr, conf=0.8)
+
+ min_y, max_y = self.roi
+ name_boxes = []
+ for r in results:
+ for box in r.boxes:
+ x1, y1, x2, y2 = box.xyxy.cpu().numpy()[0].tolist()
+ if y1 >= min_y and y2 <= max_y:
+ cls_id = int(box.cls.cpu().numpy()[0])
+ cls_name = self.lead_name_model.names[cls_id]
+ name_boxes.append({'bbox': [x1, y1, x2, y2], 'class_name': cls_name})
+ self.lead_name_bboxes = name_boxes
+
+ def extract_reference_pulses(self):
+ """Detect calibration pulse boxes with ``pulse_model``.
+
+ Runs at confidence 0.7 (lower than lead detection to improve
+ recall on small pulses). For each detection, stores the
+ corresponding image crop alongside the bounding-box coordinates.
+ Sets ``self.reference_pulses`` as a list of
+ ``{'bbox': [x1, y1, x2, y2], 'image': np.ndarray}`` dicts.
+ """
+ image_bgr = cv2.cvtColor(self.processed_image, cv2.COLOR_GRAY2BGR)
+ results = self._predict_safe(self.pulse_model, image_bgr, conf=0.7)
+
+ pulse_boxes = []
+ for r in results:
+ for box in r.boxes:
+ coord = box.xyxy.cpu().numpy()[0].tolist()
+ pulse_boxes.append({
+ 'image': self.image[int(coord[1]) - 5:int(coord[3]) + 5,
+ int(coord[0]) - 5:int(coord[2]) + 5],
+ 'bbox': coord,
+ })
+ self.reference_pulses = pulse_boxes
+
+ # ------------------------------------------------------------------
+ # Visualisation helpers
+ # ------------------------------------------------------------------
+
+ def visualize_boxes(self, task='Lead name', show_axis=False):
+ """Overlay detection bounding boxes on the original image.
+
+ Parameters
+ ----------
+ task : {'Lead name', 'Lead box', 'Reference pulse'}
+ Which set of boxes to draw.
+ show_axis : bool, optional
+ Whether to display axis ticks (default ``False``).
+ """
+ img_copy = cv2.cvtColor(self.image.copy(), cv2.COLOR_GRAY2BGR)
+
+ if task == 'Lead name':
+ if not self.lead_name_bboxes:
+ print("Lead name boxes not extracted.")
+ return
+ for box in self.lead_name_bboxes:
+ x1, y1, x2, y2 = map(int, box['bbox'])
+ cv2.rectangle(img_copy, (x1, y1), (x2, y2), (255, 0, 0), 2)
+ cv2.putText(img_copy, box['class_name'], (x1, y1 - 10),
+ cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3)
+
+ elif task == 'Lead box':
+ if not self.lead_bboxes:
+ print("Lead boxes not extracted.")
+ return
+ for bbox in self.lead_bboxes:
+ x1, y1, x2, y2 = map(int, bbox)
+ cv2.rectangle(img_copy, (x1, y1), (x2, y2), (255, 0, 0), 2)
+
+ elif task == 'Reference pulse':
+ if not self.reference_pulses:
+ print("Reference pulses not extracted.")
+ return
+ for bbox in self.reference_pulses:
+ x1, y1, x2, y2 = map(int, bbox['bbox'])
+ cv2.rectangle(img_copy, (x1, y1), (x2, y2), (255, 0, 0), 2)
+
+ else:
+ print(f"Unknown task: {task}")
+ return
+
+ plt.figure(figsize=(12, 10))
+ plt.imshow(cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB))
+ if not show_axis:
+ plt.axis('off')
+ plt.show()
+
+ def visualize_segmentation(self, show_boxes=False, show_axis=False,
+ fill_mask=True, thickness=1):
+ """Render all segmented lead polygons on the original image.
+
+ Aggregates results from all three segmentation passes in
+ ``self.lead_segmentation`` and passes them to
+ ``patched_yolo_infer.visualize_results``.
+
+ Parameters
+ ----------
+ show_boxes : bool, optional
+ Also draw bounding boxes around each polygon (default ``False``).
+ show_axis : bool, optional
+ Display axis ticks (default ``False``).
+ fill_mask : bool, optional
+ Fill polygon interiors with a translucent colour (default ``True``).
+ thickness : int, optional
+ Polygon outline thickness in pixels (default ``1``).
+ """
+ all_confidences, all_boxes, all_polygons = [], [], []
+ all_classes_ids, all_classes_names = [], []
+
+ for seg in self.lead_segmentation:
+ all_confidences.extend(seg.filtered_confidences)
+ all_boxes.extend(seg.filtered_boxes)
+ all_polygons.extend(seg.filtered_polygons)
+ all_classes_ids.extend(seg.filtered_classes_id)
+ all_classes_names.extend(seg.filtered_classes_names)
+
+ visualize_results(
+ img=cv2.cvtColor(self.image, cv2.COLOR_GRAY2RGB),
+ confidences=all_confidences,
+ boxes=all_boxes,
+ polygons=all_polygons,
+ classes_ids=all_classes_ids,
+ classes_names=all_classes_names,
+ segment=True,
+ thickness=thickness,
+ fill_mask=fill_mask,
+ show_boxes=show_boxes,
+ show_class=False,
+ axis_off=(not show_axis),
+ )
+
+ # ------------------------------------------------------------------
+ # Calibration (volt/pixel & time/pixel)
+ # ------------------------------------------------------------------
+
+ def get_reference_scale(self):
+ """Derive pixel-to-physical calibration constants from the calibration pulses.
+
+ For each pulse in ``self.reference_pulses``, the method:
+
+ 1. Enhances contrast with CLAHE and binarises with multi-Otsu thresholding.
+ 2. Detects horizontal and vertical lines via morphological filtering and
+ Hough transform to measure the pulse amplitude (voltage) and width (time).
+ 3. Refines the time calibration using the Line Segment Detector (LSD).
+
+ Sets
+ ----
+ self.volt_per_pixel : float
+ Millivolts per pixel (vertical axis). Calibrated to the standard
+ 1 mV / 10 mm calibration pulse.
+ self.time_per_pixel : float
+ Seconds per pixel (horizontal axis). Calibrated to 0.2 s between
+ the two inner vertical edges of the calibration pulse.
+
+ Raises
+ ------
+ RuntimeError
+ If no valid pulse yields voltage or time measurements.
+ """
+ voltages, times, dist = [], [], []
+
+ def _line_length(x1, y1, x2, y2):
+ return np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+
+ for pulse_idx, pulse in enumerate(self.reference_pulses):
+ try:
+ img = pulse['image']
+ if img is None or img.size == 0:
+ continue
+ h, w = img.shape
+ if h < 10 or w < 10:
+ continue
+
+ clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+ enhanced = clahe.apply(img)
+ blurred = cv2.GaussianBlur(enhanced, (3, 3), 0)
+
+ thresholds = threshold_multiotsu(blurred, classes=2)
+ regions = np.digitize(blurred, bins=thresholds)
+ binary = (regions == 0).astype(np.uint8) * 255
+
+ h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 1))
+ v_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 25))
+ h_lines_img = cv2.morphologyEx(binary, cv2.MORPH_OPEN, h_kernel)
+ v_lines_img = cv2.morphologyEx(binary, cv2.MORPH_OPEN, v_kernel)
+
+ h_lines_raw = cv2.HoughLinesP(
+ h_lines_img, rho=1, theta=np.pi / 180,
+ threshold=w // 4, minLineLength=w // 2, maxLineGap=1
+ )
+ v_lines_raw = cv2.HoughLinesP(
+ v_lines_img, rho=1, theta=np.pi / 180,
+ threshold=h // 4, minLineLength=h // 2, maxLineGap=1
+ )
+
+ combined_lines = []
+ if h_lines_raw is not None:
+ combined_lines.extend(h_lines_raw)
+ if v_lines_raw is not None:
+ combined_lines.extend(v_lines_raw)
+
+ horizontal_lines, vertical_lines = [], []
+ for line in combined_lines:
+ x1, y1, x2, y2 = line[0]
+ angle = np.arctan2(abs(y2 - y1), abs(x2 - x1)) * 180 / np.pi
+ if angle < 10 or angle > 170:
+ horizontal_lines.append((x1, y1, x2, y2))
+ elif 80 < angle < 100:
+ vertical_lines.append((x1, y1, x2, y2))
+
+ if len(vertical_lines) >= 2:
+ longest_v = sorted(vertical_lines,
+ key=lambda l: _line_length(*l), reverse=True)[:2]
+ xc1 = (longest_v[0][0] + longest_v[0][2]) / 2
+ xc2 = (longest_v[1][0] + longest_v[1][2]) / 2
+ vertical_spacing = abs(xc1 - xc2)
+
+ volt_vals = sorted([_line_length(*l) for l in vertical_lines],
+ reverse=True)[:2]
+ if volt_vals:
+ voltages.append(volt_vals)
+ if vertical_spacing > 5:
+ times.append(vertical_spacing)
+
+ # LSD for more precise time calibration
+ lsd = cv2.createLineSegmentDetector(refine=2)
+ lsd_lines, _, _, _ = lsd.detect(v_lines_img)
+
+ min_length = h / 3
+ angle_tol_rad = np.deg2rad(5)
+ filtered = []
+
+ if lsd_lines is not None:
+ for line in lsd_lines:
+ x1, y1, x2, y2 = line[0]
+ length = np.hypot(x2 - x1, y2 - y1)
+ if length >= min_length:
+ angle = np.arctan2(y2 - y1, x2 - x1)
+ if np.abs(np.abs(angle) - np.pi / 2) <= angle_tol_rad:
+ filtered.append([[x1, y1, x2, y2]])
+
+ if len(filtered) >= 4:
+ filtered.sort(
+ key=lambda l: np.hypot(l[0][2] - l[0][0], l[0][3] - l[0][1]),
+ reverse=True
+ )
+ top4 = filtered[:4]
+ top4.sort(key=lambda l: (l[0][0] + l[0][2]) / 2)
+
+ midpoints = []
+ for l1, l2 in [(top4[0], top4[1]), (top4[2], top4[3])]:
+ xc1 = (l1[0][0] + l1[0][2]) / 2
+ xc2 = (l2[0][0] + l2[0][2]) / 2
+ midpoints.append((xc1 + xc2) / 2)
+
+ dist.append(midpoints[1] - midpoints[0])
+
+ except Exception as e:
+ print(f" โ Skipping pulse {pulse_idx}: {e}", flush=True)
+ continue
+
+ if not voltages:
+ raise RuntimeError("No valid reference pulses found for voltage calibration.")
+ if not dist:
+ raise RuntimeError("No valid reference pulses found for time calibration.")
+
+ self.volt_per_pixel = 1 / np.mean(voltages)
+ self.time_per_pixel = 0.2 / np.mean(dist)
+
+ # ------------------------------------------------------------------
+ # Layout helpers
+ # ------------------------------------------------------------------
+
+ def make_bounding_box_features(self, box, axis):
+ """Extract three positional features from a bounding box along one axis.
+
+ Parameters
+ ----------
+ box : list[float]
+ Bounding box as ``[x1, y1, x2, y2]``.
+ axis : {'x', 'y'}
+ Axis along which to compute features.
+
+ Returns
+ -------
+ list[float]
+ ``[axis_min, axis_center, axis_max]``.
+ """
+ if axis == 'y':
+ axis_min, axis_max = box[1], box[3]
+ else:
+ axis_min, axis_max = box[0], box[2]
+ axis_center = (axis_min + axis_max) / 2
+ return [axis_min, axis_center, axis_max]
+
+ def bounding_boxes_kmeans(self, bounding_boxes, axis='y',
+ k_min=1, k_max=13, return_model=True):
+ """Cluster bounding boxes along one axis using K-Means.
+
+ Tries all values of *k* in ``[k_min, k_max]`` and selects the one
+ that maximises the silhouette score. Cluster labels are sorted by
+ mean position along *axis* (ascending).
+
+ Parameters
+ ----------
+ bounding_boxes : list[list[float]]
+ List of ``[x1, y1, x2, y2]`` boxes.
+ axis : {'x', 'y'}, optional
+ Axis along which to cluster (default ``'y'``).
+ k_min : int, optional
+ Minimum number of clusters (default ``1``).
+ k_max : int, optional
+ Maximum number of clusters (default ``13``).
+ return_model : bool, optional
+ If ``True``, also return the fitted ``KMeans`` model and the
+ label remapping dict (default ``True``).
+
+ Returns
+ -------
+ sorted_labels : np.ndarray of int
+ Cluster index (sorted) for each input box.
+ best_k : int
+ Optimal number of clusters.
+ sorted_centers : np.ndarray
+ Mean positional value of each cluster, sorted ascending.
+ label_map : dict
+ Mapping from original K-Means label โ sorted label.
+ best_model : sklearn.cluster.KMeans
+ Fitted model (only when ``return_model=True``).
+
+ Raises
+ ------
+ ValueError
+ If *axis* is not ``'x'`` or ``'y'``, or if there are fewer
+ boxes than *k_min*.
+ """
+ if axis not in ('x', 'y'):
+ raise ValueError("Axis must be 'x' or 'y'")
+ if len(bounding_boxes) < k_min:
+ raise ValueError("Not enough bounding boxes to cluster")
+
+ features = np.array([self.make_bounding_box_features(b, axis)
+ for b in bounding_boxes])
+
+ best_score, best_k = -1, k_min
+ best_labels = best_centers = best_model = None
+
+ for k in range(k_min, min(k_max + 1, len(bounding_boxes))):
+ kmeans = KMeans(n_clusters=k, random_state=42, n_init="auto")
+ labels = kmeans.fit_predict(features)
+ score = silhouette_score(features, labels)
+ if score > best_score:
+ best_score = score
+ best_k = k
+ best_labels = labels
+ best_centers = kmeans.cluster_centers_
+ best_model = kmeans
+
+ cluster_avgs = best_centers.mean(axis=1)
+ sorted_indices = np.argsort(cluster_avgs)
+ label_map = {old: new for new, old in enumerate(sorted_indices)}
+ sorted_labels = np.array([label_map[l] for l in best_labels])
+ sorted_centers = cluster_avgs[sorted_indices]
+
+ if return_model:
+ return sorted_labels, best_k, sorted_centers, label_map, best_model
+ return sorted_labels, best_k, sorted_centers, label_map
+
+ def check_cabrera(self, num_rows, num_cols):
+ """Determine whether the ECG uses Cabrera lead ordering.
+
+ The heuristic varies by layout:
+
+ * **12- or 6-row layouts:** compares the vertical spacing of
+ augmented-limb leads (aVR/aVL/aVF) against precordial leads.
+ Cabrera re-orders the limb leads so their spacing differs.
+ * **4-row, 3-col / 5-row layouts:** checks the vertical spread
+ of augmented leads (high std โ interleaved โ Cabrera).
+ * **3-row / 4-col layouts:** checks the horizontal spread of
+ augmented leads.
+
+ Sets ``self.is_cabrera`` as a side-effect.
+
+ Parameters
+ ----------
+ num_rows : int
+ Number of detected lead rows.
+ num_cols : int
+ Number of detected lead columns.
+
+ Returns
+ -------
+ bool
+ ``True`` if Cabrera ordering is detected.
+ """
+ if num_rows in [13, 12, 7, 6]:
+ av_leads = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'aVR', 'aVL', 'aVF'}]
+ v_leads = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'V1', 'V2', 'V3', 'V4', 'V5', 'V6'}]
+
+ if not av_leads or not v_leads:
+ return False
+
+ y_v = sorted([(b['bbox'][1] + b['bbox'][3]) / 2 for b in v_leads])
+ y_av = sorted([(b['bbox'][1] + b['bbox'][3]) / 2 for b in av_leads])
+
+ threshold = 30
+ diff_v = np.diff(y_v)
+ diff_av = np.diff(y_av)
+ diff_v = diff_v[np.abs(diff_v) > threshold]
+ diff_av = diff_av[np.abs(diff_av) > threshold]
+
+ if len(diff_v) == 0 or len(diff_av) == 0:
+ return False
+
+ if abs(np.min(diff_v) - np.min(diff_av)) > (0.25 * np.min(diff_v)):
+ self.is_cabrera = True
+ return True
+ self.is_cabrera = False
+ return False
+
+ elif (num_rows == 4 and num_cols == 3) or num_rows == 5:
+ av_leads = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'aVR', 'aVL', 'aVF'}]
+ if not av_leads:
+ return False
+ y_coords = [(b['bbox'][1] + b['bbox'][3]) / 2 for b in av_leads]
+ self.is_cabrera = np.std(y_coords) > 25
+ return self.is_cabrera
+
+ elif (num_rows == 4 and num_cols == 4) or num_rows == 3:
+ av_leads = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'aVR', 'aVL', 'aVF'}]
+ if not av_leads:
+ return False
+ x_coords = [(b['bbox'][0] + b['bbox'][2]) / 2 for b in av_leads]
+ self.is_cabrera = np.std(x_coords) > 25
+ return self.is_cabrera
+
+ def get_layout(self, num_rows):
+ """Map the detected row count to a standard ECG layout.
+
+ Calls :meth:`check_cabrera` to determine lead ordering, sets
+ ``self.layout`` (2-D list of lead name strings) and
+ ``self.has_calibration_pulse``, and returns the number of columns.
+
+ Supported row counts
+ --------------------
+ * 13 โ 12ร1 with calibration pulse
+ * 12 โ 12ร1 without calibration pulse
+ * 7 โ 6ร2 with calibration pulse
+ * 6 โ 6ร2 without calibration pulse
+ * 5 โ 4ร3 with calibration pulse
+ * 4 โ 3ร4 or 4ร3 (disambiguated by V-lead spatial distribution)
+ * 3 โ 3ร4 without calibration pulse
+
+ Parameters
+ ----------
+ num_rows : int
+ Number of detected lead rows.
+
+ Returns
+ -------
+ int
+ Number of lead columns in the detected layout.
+ """
+ if num_rows == 13:
+ num_cols = 1
+ self.has_calibration_pulse = True
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(12, 1, cabrera)]
+
+ elif num_rows == 12:
+ num_cols = 1
+ self.has_calibration_pulse = False
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(12, 1, cabrera)]
+
+ elif num_rows == 7:
+ num_cols = 2
+ self.has_calibration_pulse = True
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(6, 2, cabrera)]
+
+ elif num_rows == 6:
+ num_cols = 2
+ self.has_calibration_pulse = False
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(6, 2, cabrera)]
+
+ elif num_rows == 5:
+ num_cols = 3
+ self.has_calibration_pulse = True
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(4, 3, cabrera)]
+
+ elif num_rows == 4:
+ v_leads1 = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'V1', 'V2', 'V3'}]
+ v_leads2 = [b for b in self.lead_name_bboxes
+ if b['class_name'] in {'V4', 'V5', 'V6'}]
+
+ def centers(boxes, axis):
+ if axis == 'x':
+ return [(b['bbox'][0] + b['bbox'][2]) / 2 for b in boxes]
+ return [(b['bbox'][1] + b['bbox'][3]) / 2 for b in boxes]
+
+ x_std1 = np.std(centers(v_leads1, 'x'))
+ y_std1 = np.std(centers(v_leads1, 'y'))
+ x_std2 = np.std(centers(v_leads2, 'x'))
+ y_std2 = np.std(centers(v_leads2, 'y'))
+
+ if x_std1 < y_std1 and x_std2 < y_std2:
+ num_cols = 4
+ self.has_calibration_pulse = True
+ elif y_std1 < x_std1 and y_std2 < x_std2:
+ num_cols = 3
+ self.has_calibration_pulse = False
+ else:
+ num_cols = 4
+ self.has_calibration_pulse = True
+
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ key = (3, 4, cabrera) if num_cols == 4 else (4, 3, cabrera)
+ self.layout = self.standard_layouts[key]
+
+ elif num_rows == 3:
+ num_cols = 4
+ self.has_calibration_pulse = False
+ cabrera = self.check_cabrera(num_rows, num_cols)
+ self.layout = self.standard_layouts[(3, 4, cabrera)]
+
+ return num_cols
+
+ def clean_lead_mask(self, img, baseline_y):
+ """Remove leaked segments of neighboring leads from a single lead cell binary mask.
+
+ Parameters
+ ----------
+ img : np.ndarray
+ Binary mask for a single lead cell (H ร W).
+ baseline_y : float
+ Y-coordinate of the row baseline relative to the cropped row mask.
+
+ Returns
+ -------
+ np.ndarray
+ Cleaned binary mask.
+ """
+ height, width = img.shape
+ if height < 10 or width < 10:
+ return img
+
+ num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img)
+ if num_labels <= 2:
+ return img
+
+ # Find the primary component (largest horizontal span)
+ primary_c = -1
+ max_w = -1
+ for c in range(1, num_labels):
+ w = stats[c, cv2.CC_STAT_WIDTH]
+ if w > max_w:
+ max_w = w
+ primary_c = c
+
+ if primary_c == -1:
+ return img
+
+ primary_area = stats[primary_c, cv2.CC_STAT_AREA]
+ cleaned_img = img.copy()
+
+ for c in range(1, num_labels):
+ if c == primary_c:
+ continue
+
+ c_top = stats[c, cv2.CC_STAT_TOP]
+ c_height = stats[c, cv2.CC_STAT_HEIGHT]
+ c_bottom = c_top + c_height
+ c_width = stats[c, cv2.CC_STAT_WIDTH]
+ c_area = stats[c, cv2.CC_STAT_AREA]
+ c_centroid_y = centroids[c][1]
+
+ # Heuristics to identify a leak:
+ # 1. It is relatively small (either narrow or small area)
+ is_small = (c_width < 0.35 * width) or (c_area < 0.25 * primary_area)
+
+ # 2. It is located near the vertical boundaries of the cell
+ near_boundary = (c_top < 0.15 * height) or (c_bottom > 0.85 * height)
+
+ # 3. It doesn't cross the baseline (with safety margin)
+ baseline_margin = max(5, int(0.08 * height))
+ crosses_baseline = (c_top - baseline_margin <= baseline_y <= c_bottom + baseline_margin)
+
+ # 4. It is far from the baseline
+ far_from_baseline = abs(c_centroid_y - baseline_y) > 0.2 * height
+
+ if is_small and near_boundary and not crosses_baseline and far_from_baseline:
+ cleaned_img[labels == c] = 0
+
+ return cleaned_img
+
+ # ------------------------------------------------------------------
+ # Grid construction
+ # ------------------------------------------------------------------
+
+ def make_grid(self, padding=0):
+ """Build the lead mask grid from segmentation polygons and lead boxes.
+
+ Steps:
+
+ 1. Assigns each segmented polygon to the nearest row centre by
+ polygon centroid Y-coordinate.
+ 2. Crops each row mask to its vertical extent.
+ 3. Clusters lead bounding boxes into *num_cols* columns with K-Means.
+ 4. Slices each (row, col) cell from the corresponding row mask.
+ 5. Applies a morphological opening to each cell slice.
+
+ Sets
+ ----
+ self.grid : list[list[dict]]
+ ``grid[row][col]`` contains ``{'lead': str, 'signal': np.ndarray}``,
+ where ``'signal'`` is currently a binary mask slice.
+ self.baseline : list[float]
+ Relative Y-position of the isoelectric baseline within each row
+ (pixels from the top of the cropped row mask).
+ self.row : dict[int, np.ndarray]
+ Cropped binary mask for each row index.
+
+ Parameters
+ ----------
+ padding : int, optional
+ Extra pixel padding (currently unused, reserved for future use).
+ """
+ image_height, image_width = self.image.shape[:2]
+ num_rows = len(self.row_centers)
+ num_cols = self.get_layout(num_rows)
+
+ row_masks = {i: np.zeros((image_height, image_width), dtype=np.uint8)
+ for i in range(num_rows)}
+ row_polygons = {i: [] for i in range(num_rows)}
+ row_limits = {i: [] for i in range(num_rows)}
+
+ for seg in self.lead_segmentation:
+ for box, polygon in zip(seg.filtered_boxes, seg.filtered_polygons):
+ poly_np = np.array(polygon, dtype=np.int32).reshape((-1, 1, 2))
+ temp_mask = np.zeros((image_height, image_width), dtype=np.uint8)
+ cv2.fillPoly(temp_mask, [poly_np], color=1)
+
+ temp_proj = np.sum(temp_mask, axis=1)
+ total_weight_y = np.sum(temp_proj)
+ if total_weight_y == 0:
+ continue
+
+ centroid_y = int(np.sum(np.arange(temp_proj.shape[0]) * temp_proj)
+ / total_weight_y)
+
+ y_vals = [pt[1] for pt in polygon]
+ min_y, max_y = min(y_vals), max(y_vals)
+
+ if max_y < self.first_peak_start or min_y > self.last_peak_end:
+ continue
+
+ closest_idx = int(np.argmin(np.abs(self.row_centers - centroid_y)))
+
+ if self.has_calibration_pulse and closest_idx == num_rows - 1:
+ continue
+
+ row_polygons[closest_idx].append(polygon)
+ cv2.fillPoly(row_masks[closest_idx], [poly_np], color=1)
+
+ if not row_limits[closest_idx]:
+ row_limits[closest_idx] = [min_y, max_y]
+ else:
+ row_limits[closest_idx][0] = min(row_limits[closest_idx][0], min_y)
+ row_limits[closest_idx][1] = max(row_limits[closest_idx][1], max_y)
+
+ cropped_row_masks = {}
+ for i in range(num_rows):
+ if self.has_calibration_pulse and i == num_rows - 1:
+ continue
+ if not row_limits[i]:
+ continue
+ min_y, max_y = row_limits[i]
+ cropped_row_masks[i] = row_masks[i][min_y:max_y + 1, :]
+
+ lead_boxes = [
+ box for box in self.lead_bboxes
+ if not (
+ self.has_calibration_pulse and
+ int(np.argmin(np.abs(self.row_centers - (box[1] + box[3]) / 2)))
+ == num_rows - 1
+ )
+ ] if self.has_calibration_pulse else self.lead_bboxes
+
+ self.row = cropped_row_masks
+
+ if num_cols != 1:
+ labels_cols, _, _, _ = self.bounding_boxes_kmeans(
+ lead_boxes, axis='x', k_min=num_cols, k_max=num_cols, return_model=False
+ )
+ else:
+ labels_cols = np.zeros(len(lead_boxes), dtype=int)
+
+ boxes_arr = np.array(lead_boxes)
+ min_x_per_col, max_x_per_col = [], []
+ for col_label in range(num_cols):
+ col_boxes = boxes_arr[labels_cols == col_label]
+ min_x_per_col.append(col_boxes[:, 0].min())
+ max_x_per_col.append(col_boxes[:, 2].max())
+
+ mask_grid, relative_baselines = [], []
+ for row_idx, row_slice in cropped_row_masks.items():
+ row_cells = []
+ for col_idx in range(num_cols):
+ x_min = max(0, int(min_x_per_col[col_idx]))
+ x_max = min(image_width, int(max_x_per_col[col_idx]))
+ cell_slice = row_slice[:, x_min:x_max]
+
+ kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+ cell_slice = cv2.morphologyEx(cell_slice, cv2.MORPH_OPEN, kernel)
+
+ # Post-processing: Clean up leaked segments from neighboring leads
+ baseline_y = self.row_centers[row_idx] - row_limits[row_idx][0]
+ cell_slice = self.clean_lead_mask(cell_slice, baseline_y)
+
+ row_cells.append({
+ 'lead': self.layout[row_idx][col_idx],
+ 'signal': cell_slice,
+ })
+
+ relative_baselines.append(
+ self.row_centers[row_idx] - row_limits[row_idx][0]
+ )
+ mask_grid.append(row_cells)
+
+ self.grid = mask_grid
+ self.baseline = relative_baselines
+
+ def visualize_grid(self, figsize=(15, 10)):
+ """Display the lead mask grid โ one subplot per (row, col) cell.
+
+ Parameters
+ ----------
+ figsize : tuple[int, int], optional
+ Overall figure size in inches (default ``(15, 10)``).
+
+ Raises
+ ------
+ ValueError
+ If :meth:`make_grid` has not been called yet.
+ """
+ if not hasattr(self, 'grid') or not self.grid:
+ raise ValueError("Grid not generated. Call make_grid() first.")
+
+ num_rows = len(self.grid)
+ num_cols = len(self.grid[0]) if isinstance(self.grid[0], list) else 1
+
+ fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
+ if num_cols == 1:
+ axes = np.atleast_2d(axes).T
+ elif num_rows == 1:
+ axes = np.atleast_2d(axes)
+
+ for row_idx in range(num_rows):
+ row = self.grid[row_idx] if isinstance(self.grid[row_idx], list) \
+ else [self.grid[row_idx]]
+ for col_idx, cell in enumerate(row):
+ ax = axes[row_idx][col_idx]
+ ax.imshow(cell['signal'], cmap='gray', aspect='auto')
+ ax.set_title(cell['lead'], fontsize=10)
+ ax.axis('off')
+
+ plt.tight_layout()
+ plt.show()
+
+ # ------------------------------------------------------------------
+ # Signal extraction
+ # ------------------------------------------------------------------
+
+ def binarize_signal(self, img, window_length=11, polyorder=2):
+ """Extract a 1-D signal trace from a binary lead mask column by column.
+
+ For each image column, computes the intensity-weighted centroid of
+ non-zero pixels. Uses the second derivative of the initial centroid
+ trace (Savitzky-Golay filtered) to disambiguate ambiguous columns:
+ upward concavity โ take the topmost pixels; downward concavity โ
+ take the bottommost pixels; otherwise use all non-zero pixels.
+
+ Parameters
+ ----------
+ img : np.ndarray
+ Binary uint8 mask for a single lead cell (H ร W).
+ window_length : int, optional
+ Savitzky-Golay filter window (must be odd, default ``11``).
+ polyorder : int, optional
+ Savitzky-Golay polynomial order (default ``2``).
+
+ Returns
+ -------
+ x_coords : np.ndarray of int
+ Column indices where a signal was detected.
+ final_signal : np.ndarray of float
+ Weighted-centroid Y-coordinate for each column in *x_coords*.
+ """
+ height, width = img.shape
+ x_coords = []
+ initial_signal = []
+
+ for col in range(width):
+ column = img[:, col]
+ if np.sum(column) == 0:
+ continue
+
+ y_indices = np.arange(height)
+ weights = column.astype(float)
+ centroid = np.average(y_indices, weights=weights)
+ initial_signal.append(centroid)
+ x_coords.append(col)
+
+ x_coords = np.array(x_coords)
+ initial_signal = np.array(initial_signal)
+
+ second_deriv = np.gradient(np.gradient(savgol_filter(initial_signal, window_length, polyorder)))
+ second_deriv = savgol_filter(second_deriv, 11, 3)
+
+ x_out, final_signal = [], []
+ for idx, col in enumerate(x_coords):
+ column = img[:, col]
+ nz_idx = np.where(column > 0)[0]
+ if nz_idx.size == 0:
+ continue
+
+ if second_deriv[idx] > 0.5:
+ sel_idx = nz_idx[:5]
+ elif second_deriv[idx] < -0.5:
+ sel_idx = nz_idx[-5:]
+ else:
+ sel_idx = nz_idx
+
+ weights = column[sel_idx].astype(float)
+ centroid = np.average(sel_idx, weights=weights)
+ x_out.append(col)
+ final_signal.append(centroid)
+
+ return np.array(x_out), np.array(final_signal)
+
+ def fill_gaps(self, x_coords, y_coords, method='linear'):
+ """Interpolate missing columns to produce a dense, evenly-spaced signal.
+
+ Parameters
+ ----------
+ x_coords : np.ndarray of int
+ Sparse column indices from :meth:`binarize_signal`.
+ y_coords : np.ndarray of float
+ Corresponding centroid Y-values.
+ method : str, optional
+ Interpolation kind passed to ``scipy.interpolate.interp1d``
+ (default ``'linear'``).
+
+ Returns
+ -------
+ x_full : np.ndarray of int
+ Dense array ``[x_coords[0], โฆ, x_coords[-1]]``.
+ y_interp : np.ndarray of float
+ Interpolated Y-values at every integer column.
+ """
+ x_full = np.arange(x_coords[0], x_coords[-1] + 1)
+ interpolator = interp1d(x_coords, y_coords, kind=method, fill_value="extrapolate")
+ y_interp = interpolator(x_full)
+ return x_full, y_interp
+
+ def smooth_signal(self, sig, window_length=7, polyorder=4):
+ """Apply a Savitzky-Golay low-pass filter to a 1-D signal.
+
+ Parameters
+ ----------
+ sig : np.ndarray
+ Input signal array.
+ window_length : int, optional
+ Filter window size in samples (must be odd, default ``7``).
+ polyorder : int, optional
+ Polynomial order (default ``4``).
+
+ Returns
+ -------
+ np.ndarray
+ Smoothed signal of the same length as *sig*.
+ """
+ return savgol_filter(sig, window_length, polyorder)
+
+ def extract_signals(self):
+ """Convert mask grid cells into calibrated voltage time-series.
+
+ For each cell in ``self.grid``:
+
+ 1. Calls :meth:`binarize_signal` to get column-centroid Y-values.
+ 2. Trims 5 edge samples, fills gaps, and smooths with Savitzky-Golay.
+ 3. Converts pixel Y-coordinates to millivolts using
+ ``self.baseline`` and ``self.volt_per_pixel``.
+ 4. Converts column indices to seconds using ``self.time_per_pixel``.
+ 5. Resamples uniformly at 500 Hz via linear interpolation.
+
+ Sets ``self.signal_grid`` with the same structure as ``self.grid``
+ but each cell additionally contains:
+
+ * ``'time'`` โ np.ndarray of time values in seconds.
+ * ``'signal'`` โ np.ndarray of voltage values in millivolts.
+ """
+ signal_grid = []
+ sample_rate = 500
+
+ for row_idx, row in enumerate(self.grid):
+ baseline_y = self.baseline[row_idx]
+ row_signals = []
+
+ for cell in row:
+ x_coords, sig = self.binarize_signal(cell['signal'])
+
+ x_coords = x_coords[5:-5]
+ sig = sig[5:-5]
+
+ x_coords, sig = self.fill_gaps(x_coords, sig, method='linear')
+ sig = self.smooth_signal(sig)
+
+ signal_volts = (baseline_y - sig) * self.volt_per_pixel
+ x_seconds = x_coords * self.time_per_pixel
+
+ duration = x_seconds[-1] - x_seconds[0]
+ num_samples = round(duration * sample_rate) + 1
+ resampled_time = np.linspace(x_seconds[0], x_seconds[-1], num_samples)
+
+ interpolator = interp1d(x_seconds, signal_volts, kind='linear',
+ fill_value="extrapolate")
+ resampled_signal = interpolator(resampled_time)
+
+ row_signals.append({
+ 'lead': cell['lead'],
+ 'time': resampled_time,
+ 'signal': resampled_signal
+ })
+
+ signal_grid.append(row_signals)
+
+ self.signal_grid = signal_grid
+
+ # ------------------------------------------------------------------
+ # Metrics & comparison
+ # ------------------------------------------------------------------
+
+ def sliding_metrics(self, signal_a, signal_b, return_aligned_signals=False):
+ """Compute alignment-aware similarity metrics between two signals.
+
+ Slides the shorter signal across the longer one and finds the offset
+ that maximises the Pearson correlation. Reports RMSE and SNR at
+ the best-aligned position.
+
+ Parameters
+ ----------
+ signal_a, signal_b : array-like
+ The two signals to compare (need not be the same length).
+ return_aligned_signals : bool, optional
+ If ``True``, also return the aligned signal pair (default ``False``).
+
+ Returns
+ -------
+ max_corr : float
+ Maximum Pearson correlation found across all offsets.
+ rmse : float
+ Root mean squared error at the best offset.
+ snr : float
+ Signal-to-noise ratio in dB at the best offset
+ (``inf`` if noise power is zero).
+ short_signal : np.ndarray
+ The shorter signal (only when *return_aligned_signals* is ``True``).
+ best_window : np.ndarray
+ The aligned window from the longer signal
+ (only when *return_aligned_signals* is ``True``).
+ """
+ len_a, len_b = len(signal_a), len(signal_b)
+ if len_a > len_b:
+ long_signal, short_signal = signal_a, signal_b
+ else:
+ long_signal, short_signal = signal_b, signal_a
+
+ len_long, len_short = len(long_signal), len(short_signal)
+ max_corr, best_corr_offset = -1, 0
+
+ for i in range(len_long - len_short + 1):
+ window = long_signal[i:i + len_short]
+ corr, _ = pearsonr(window, short_signal)
+ if corr > max_corr:
+ max_corr = corr
+ best_corr_offset = i
+
+ best_window = long_signal[best_corr_offset:best_corr_offset + len_short]
+ rmse = mean_squared_error(best_window, short_signal)
+ signal_power = np.mean(np.square(short_signal))
+ noise_power = np.mean(np.square(best_window - short_signal))
+ snr = (10 * np.log10(signal_power / noise_power)
+ if noise_power > 0 else np.inf)
+
+ if return_aligned_signals:
+ return max_corr, rmse, snr, short_signal, best_window
+ return max_corr, rmse, snr
+
+ def calculate_metrics_ptb(self, plot_signals=True, per_lead_scores=None):
+ """Evaluate digitization quality against a WFDB ground-truth record.
+
+ For each lead in ``self.signal_grid``, aligns the extracted signal
+ to the corresponding WFDB signal using :meth:`sliding_metrics` and
+ records Pearson r, RMSE, SNR, and p-value. Requires ``self.wfdb_path``
+ to point to a valid WFDB record.
+
+ Sets the following instance attributes after completion:
+
+ * ``self.average_pearson`` โ mean Pearson r across all leads.
+ * ``self.average_rmse`` โ mean RMSE (mV).
+ * ``self.average_snr`` โ mean SNR (dB).
+ * ``self.average_pval`` โ mean p-value.
+
+ Parameters
+ ----------
+ plot_signals : bool, optional
+ Unused placeholder (default ``True``).
+ per_lead_scores : dict or None, optional
+ If provided, per-lead metrics are appended here for leads where
+ Pearson r > 0.60. Expected structure:
+ ``{lead_name: {'pearson': [], 'rmse': [], 'snr': [], 'pval': []}}``.
+ """
+ record = wfdb.rdrecord(self.wfdb_path)
+ avg_pearson, avg_rmse, avg_snr, avg_pval = [], [], [], []
+
+ for row in self.signal_grid:
+ for cell in row:
+ if 'lead' not in cell or 'signal' not in cell:
+ continue
+ try:
+ lead_index = record.sig_name.index(cell['lead'])
+ except ValueError:
+ continue
+
+ wfdb_signal = record.p_signal[:, lead_index]
+ wfdb_signal = wfdb_signal[~np.isnan(wfdb_signal)]
+ voltage_signal = np.array(cell['signal'])
+
+ pearson_val, rmse, snr, sig1, sig2 = self.sliding_metrics(
+ voltage_signal, wfdb_signal, return_aligned_signals=True
+ )
+
+ try:
+ pearson, pval = pearsonr(sig1, sig2)
+ except Exception:
+ pearson, pval = np.nan, np.nan
+
+ cell.update({'pearson': pearson, 'rmse': rmse,
+ 'snr': snr, 'pval': pval})
+
+ avg_pearson.append(pearson)
+ avg_rmse.append(rmse)
+ avg_snr.append(snr)
+ avg_pval.append(pval)
+
+ if per_lead_scores is not None and pearson > 0.60:
+ lead = cell['lead']
+ if lead not in per_lead_scores:
+ per_lead_scores[lead] = {
+ 'pearson': [], 'rmse': [], 'snr': [], 'pval': []
+ }
+ per_lead_scores[lead]['pearson'].append(pearson)
+ per_lead_scores[lead]['rmse'].append(rmse)
+ per_lead_scores[lead]['snr'].append(snr)
+ per_lead_scores[lead]['pval'].append(pval)
+
+ self.average_pearson = np.mean(avg_pearson)
+ self.average_rmse = np.mean(avg_rmse)
+ self.average_snr = np.mean(avg_snr)
+ self.average_pval = np.mean(avg_pval)
+
+ def plot_signals(self, title='', plot_wfdb=False):
+ """Plot extracted signals, optionally overlaid with the WFDB ground truth.
+
+ Generates one figure per lead. When *plot_wfdb* is ``True``, the
+ extracted signal and the best-aligned WFDB window are plotted
+ together for visual comparison.
+
+ Parameters
+ ----------
+ title : str, optional
+ Figure title applied to every subplot (default ``''``).
+ plot_wfdb : bool, optional
+ If ``True``, overlay the ground-truth WFDB signal. Requires
+ ``self.wfdb_path`` and a loaded ``record`` in scope
+ (default ``False``).
+ """
+ for row in self.signal_grid:
+ for cell in row:
+ voltage_signal = cell['signal']
+ plt.figure(figsize=(10, 4))
+ if not plot_wfdb:
+ plt.plot(voltage_signal, linewidth=1.5)
+ else:
+ lead_index = record.sig_name.index(cell['lead'])
+ wfdb_signal = record.p_signal[:, lead_index]
+ wfdb_signal = [x for x in wfdb_signal if not np.isnan(x)]
+ _, _, _, sig1, sig2 = self.sliding_metrics(
+ voltage_signal, wfdb_signal, return_aligned_signals=True
+ )
+ plt.plot(sig1, label='Extracted Signal', linewidth=1.5)
+ plt.plot(sig2, label='Ground Truth', linewidth=1.5)
+ plt.title(title)
+ plt.legend()
+ plt.xlabel("Time (ms)")
+ plt.ylabel("Voltage (mV)")
+ plt.tight_layout()
+ plt.show()
+
+ # ------------------------------------------------------------------
+ # Export
+ # ------------------------------------------------------------------
+
+ def save_signals_as_wfdb(self, record_name, directory='.'):
+ """Export extracted signals as a WFDB record (.hea + .dat files).
+
+ Pads all lead signals to the same length with NaN (converted to 0),
+ then writes a 500 Hz, 16-bit WFDB record in millivolts.
+
+ Parameters
+ ----------
+ record_name : str
+ Base name for the output files (no extension).
+ directory : str, optional
+ Destination directory (default ``'.'``).
+ """
+ signals, lead_names, max_length = [], [], 0
+ for row in self.signal_grid:
+ for cell in row:
+ if 'signal' in cell and 'lead' in cell:
+ sig = np.array(cell['signal'])
+ signals.append(sig)
+ lead_names.append(cell['lead'])
+ max_length = max(max_length, len(sig))
+
+ padded = [np.pad(s, (0, max_length - len(s)),
+ mode='constant', constant_values=np.nan)
+ for s in signals]
+ signal_array = np.nan_to_num(np.array(padded).T, nan=0.0)
+
+ wfdb.wrsamp(
+ record_name=record_name,
+ fs=500,
+ units=['mV'] * len(lead_names),
+ sig_name=lead_names,
+ p_signal=signal_array,
+ fmt=['16'] * len(lead_names),
+ write_dir=directory,
+ )
+
+ def save_signals_as_csv(self, record_name, directory='.'):
+ """
+ Save ECG signals to CSV. Each column = one lead, each row = one time sample.
+ """
+ signals, lead_names, max_length = [], [], 0
+ for row in self.signal_grid:
+ for cell in row:
+ if 'signal' in cell and 'lead' in cell:
+ sig = np.asarray(cell['signal'], dtype=float)
+ signals.append(sig)
+ lead_names.append(cell['lead'])
+ max_length = max(max_length, len(sig))
+
+ if not signals:
+ raise ValueError("No valid signals found in signal_grid.")
+
+ padded = [np.pad(s, (0, max_length - len(s)),
+ mode='constant', constant_values=np.nan)
+ for s in signals]
+ df = pd.DataFrame(np.vstack(padded).T, columns=lead_names)
+
+ os.makedirs(directory, exist_ok=True)
+ csv_path = os.path.join(directory, f"{record_name}.csv")
+ df.to_csv(csv_path, index=False, float_format="%.6f")
+
+ # ------------------------------------------------------------------
+ # Full pipeline
+ # ------------------------------------------------------------------
+
+ def run_full_pipeline(self):
+ """Run all digitization stages end-to-end for a single ECG image.
+
+ Executes the following stages in order, with per-stage wall-clock
+ timing printed to stdout via :meth:`_timed`:
+
+ 1. **Image loading & preprocessing** โ ``load_image`` + ``preprocess_image``.
+ 2. **Segmentation** โ ``segment_leads`` โ ``make_segmentation_mask``
+ โ ``find_row_centers`` โ ``get_roi``.
+ 3. **Sequential YOLO detections** โ ``extract_lead_boxes``,
+ ``extract_lead_name_boxes``, ``extract_reference_pulses``.
+ (Sequential, not parallel, to avoid a PyTorch thread-pool deadlock.)
+ 4. **Retry loop** โ if any of the three detections is empty, the
+ image is reloaded at the next fallback target size from
+ ``[2000, 2100, 1900, 2200, 1800, 1700]`` px and stages 1โ3 are
+ re-run. Raises ``RuntimeError`` if all sizes fail.
+ 5. **Calibration** โ ``get_reference_scale``.
+ 6. **Grid & signal extraction** โ ``make_grid`` โ ``extract_signals``.
+
+ Raises
+ ------
+ RuntimeError
+ If all fallback target sizes yield at least one empty detection.
+ """
+ FALLBACK_SIZES = [2000, 2100, 1900, 2200, 1800, 1700]
+
+ for target_size in FALLBACK_SIZES:
+
+ # -- Image loading & preprocessing --
+ self._timed("load_image", self.load_image, self.image_path,
+ target_size=target_size)
+ self._timed("preprocess", self.preprocess_image)
+
+ # -- Segmentation (sequential: each step depends on the previous) --
+ self._timed("segment_leads", self.segment_leads)
+ self._timed("make_mask", self.make_segmentation_mask)
+ self._timed("find_row_centers", self.find_row_centers)
+ self._timed("get_roi", self.get_roi)
+
+ # -- Three YOLO calls sequentially --
+ self._timed("extract_lead_boxes", self.extract_lead_boxes)
+ self._timed("extract_lead_name_boxes", self.extract_lead_name_boxes)
+ self._timed("extract_reference_pulses", self.extract_reference_pulses)
+
+ # -- Check all three detections; retry at next size if any is empty --
+ missing = []
+ if len(self.reference_pulses) == 0:
+ missing.append("pulses")
+ if len(self.lead_bboxes) == 0:
+ missing.append("lead boxes")
+ if len(self.lead_name_bboxes) == 0:
+ missing.append("lead names")
+
+ if not missing:
+ if target_size != FALLBACK_SIZES[0]:
+ print(f" โณ All detections found at target_size={target_size}",
+ flush=True)
+ break
+ else:
+ print(f" โณ Missing {', '.join(missing)} at target_size={target_size},"
+ f" retrying...", flush=True)
+
+ else:
+ raise RuntimeError(
+ f"Detections incomplete at all target sizes {FALLBACK_SIZES}. "
+ f"Last missing: {', '.join(missing)}."
+ )
+
+ # -- Remainder of pipeline (sequential) --
+ self._timed("get_reference_scale", self.get_reference_scale)
+ self._timed("make_grid", self.make_grid)
+ self._timed("extract_signals", self.extract_signals)
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..7d2d56b390f815dc728ff7df111431a9e4a6d833
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,194 @@
+name: infer
+channels:
+ - main
+ - r
+ - conda-forge
+ - bioconda
+ - pytorch
+dependencies:
+ - _libgcc_mutex=0.1=main
+ - _openmp_mutex=5.1=1_gnu
+ - aiohappyeyeballs=2.4.4=py39h06a4308_0
+ - aiohttp=3.11.10=py39h5eee18b_0
+ - aiosignal=1.2.0=pyhd3eb1b0_0
+ - aom=3.6.0=h6a678d5_0
+ - async-timeout=5.0.1=py39h06a4308_0
+ - attrs=24.3.0=py39h06a4308_0
+ - blas=1.0=openblas
+ - blosc=1.21.3=h6a678d5_0
+ - bottleneck=1.4.2=py39ha9d4c09_0
+ - brotli=1.0.9=h5eee18b_9
+ - brotli-bin=1.0.9=h5eee18b_9
+ - brotli-python=1.0.9=py39h6a678d5_9
+ - brunsli=0.1=h2531618_0
+ - bzip2=1.0.8=h5eee18b_6
+ - c-ares=1.19.1=h5eee18b_0
+ - ca-certificates=2025.2.25=h06a4308_0
+ - certifi=2025.4.26=py39h06a4308_0
+ - cffi=1.17.1=py39h1fdaa30_1
+ - cfitsio=3.470=h5893167_7
+ - charls=2.2.0=h2531618_0
+ - dav1d=1.2.1=h5eee18b_0
+ - freetype=2.13.3=h4a9f257_0
+ - frozenlist=1.5.0=py39h5eee18b_0
+ - fsspec=2025.3.2=py39he3bba80_0
+ - gettext=0.21.0=hedfda30_2
+ - giflib=5.2.2=h5eee18b_0
+ - icu=73.1=h6a678d5_0
+ - imagecodecs=2024.9.22=py39h652d938_1
+ - imageio=2.37.0=py39h06a4308_0
+ - importlib_resources=6.4.0=py39h06a4308_0
+ - joblib=1.4.2=py39h06a4308_0
+ - jpeg=9e=h5eee18b_3
+ - jxrlib=1.1=h7b6447c_2
+ - krb5=1.20.1=h143b758_1
+ - lazy_loader=0.4=py39h06a4308_0
+ - lcms2=2.16=h92b89f2_1
+ - ld_impl_linux-64=2.40=h12ee557_0
+ - lerc=4.0.0=h6a678d5_0
+ - libaec=1.1.3=h6a678d5_0
+ - libavif=1.1.1=h5eee18b_0
+ - libbrotlicommon=1.0.9=h5eee18b_9
+ - libbrotlidec=1.0.9=h5eee18b_9
+ - libbrotlienc=1.0.9=h5eee18b_9
+ - libcurl=8.12.1=hc9e6f67_0
+ - libdeflate=1.22=h5eee18b_0
+ - libedit=3.1.20230828=h5eee18b_0
+ - libev=4.33=h7f8727e_1
+ - libffi=3.4.4=h6a678d5_1
+ - libflac=1.3.4=h27087fc_0
+ - libgcc-ng=11.2.0=h1234567_1
+ - libgfortran-ng=11.2.0=h00389a5_1
+ - libgfortran5=11.2.0=h1234567_1
+ - libgomp=11.2.0=h1234567_1
+ - libnghttp2=1.57.0=h2d74bed_0
+ - libogg=1.3.5=h27cfd23_1
+ - libopenblas=0.3.29=ha39b09d_0
+ - libopus=1.3.1=h5eee18b_1
+ - libpng=1.6.39=h5eee18b_0
+ - libsndfile=1.0.31=h9c3ff4c_1
+ - libssh2=1.11.1=h251f7ec_0
+ - libstdcxx-ng=11.2.0=h1234567_1
+ - libtiff=4.7.0=hde9077f_0
+ - libvorbis=1.3.7=h7b6447c_0
+ - libwebp-base=1.3.2=h5eee18b_1
+ - libxml2=2.13.8=hfdd30dd_0
+ - libzopfli=1.0.3=he6710b0_0
+ - lz4-c=1.9.4=h6a678d5_1
+ - matplotlib-base=3.9.2=py39hbfdbfaf_1
+ - multidict=6.1.0=py39h5eee18b_0
+ - ncurses=6.4=h6a678d5_0
+ - networkx=3.2.1=py39h06a4308_0
+ - numexpr=2.10.1=py39hd28fd6d_0
+ - numpy=1.26.4=py39heeff2f4_0
+ - numpy-base=1.26.4=py39h8a23956_0
+ - openjpeg=2.5.2=h0d4d230_1
+ - openssl=3.0.16=h5eee18b_0
+ - pandas=2.2.3=py39h6a678d5_0
+ - pip=25.1=pyhc872135_2
+ - propcache=0.3.1=py39h5eee18b_0
+ - pybind11-abi=4=hd3eb1b0_1
+ - pycparser=2.21=pyhd3eb1b0_0
+ - pysocks=1.7.1=py39h06a4308_0
+ - pysoundfile=0.11.0=pyhd8ed1ab_0
+ - python=3.9.21=he870216_1
+ - python-dateutil=2.9.0post0=py39h06a4308_2
+ - python-tzdata=2025.2=pyhd3eb1b0_0
+ - readline=8.2=h5eee18b_0
+ - requests=2.32.3=py39h06a4308_1
+ - scikit-image=0.24.0=py39h1128e8f_0
+ - scikit-learn=1.6.1=py39h6a678d5_0
+ - scipy=1.13.1=py39heeff2f4_1
+ - setuptools=78.1.1=py39h06a4308_0
+ - six=1.17.0=py39h06a4308_0
+ - snappy=1.2.1=h6a678d5_0
+ - sqlite=3.45.3=h5eee18b_0
+ - threadpoolctl=3.5.0=py39h2f386ee_0
+ - tifffile=2023.4.12=py39h06a4308_0
+ - tk=8.6.14=h39e8969_0
+ - typing_extensions=4.12.2=py39h06a4308_0
+ - tzdata=2025b=h04d1e81_0
+ - unicodedata2=15.1.0=py39h5eee18b_1
+ - wfdb=4.3.0=pyhd8ed1ab_0
+ - wheel=0.45.1=py39h06a4308_0
+ - xz=5.6.4=h5eee18b_1
+ - yarl=1.18.0=py39h5eee18b_0
+ - zfp=1.0.0=h6a678d5_0
+ - zipp=3.21.0=py39h06a4308_0
+ - zlib=1.2.13=h5eee18b_1
+ - zlib-ng=2.0.7=h5eee18b_0
+ - zstd=1.5.6=hc292b87_0
+ - pip:
+ - asttokens==3.0.0
+ - charset-normalizer==3.4.2
+ - comm==0.2.2
+ - contourpy==1.3.0
+ - cycler==0.12.1
+ - debugpy==1.8.14
+ - decorator==5.2.1
+ - exceptiongroup==1.3.0
+ - executing==2.2.0
+ - filelock==3.18.0
+ - fonttools==4.58.0
+ - idna==3.10
+ - importlib-metadata==8.7.0
+ - importlib-resources==6.5.2
+ - ipykernel==6.29.5
+ - ipython==8.18.1
+ - jedi==0.19.2
+ - jinja2==3.1.6
+ - jupyter-client==8.6.3
+ - jupyter-core==5.7.2
+ - kiwisolver==1.4.7
+ - markupsafe==3.0.2
+ - matplotlib==3.9.4
+ - matplotlib-inline==0.1.7
+ - mpmath==1.3.0
+ - nest-asyncio==1.6.0
+ - nvidia-cublas-cu12==12.6.4.1
+ - nvidia-cuda-cupti-cu12==12.6.80
+ - nvidia-cuda-nvrtc-cu12==12.6.77
+ - nvidia-cuda-runtime-cu12==12.6.77
+ - nvidia-cudnn-cu12==9.5.1.17
+ - nvidia-cufft-cu12==11.3.0.4
+ - nvidia-cufile-cu12==1.11.1.6
+ - nvidia-curand-cu12==10.3.7.77
+ - nvidia-cusolver-cu12==11.7.1.2
+ - nvidia-cusparse-cu12==12.5.4.2
+ - nvidia-cusparselt-cu12==0.6.3
+ - nvidia-nccl-cu12==2.26.2
+ - nvidia-nvjitlink-cu12==12.6.85
+ - nvidia-nvtx-cu12==12.6.77
+ - opencv-python==4.11.0.86
+ - packaging==25.0
+ - parso==0.8.4
+ - patched-yolo-infer==1.3.8
+ - pexpect==4.9.0
+ - pillow==11.2.1
+ - platformdirs==4.3.8
+ - prompt-toolkit==3.0.51
+ - psutil==7.0.0
+ - ptyprocess==0.7.0
+ - pure-eval==0.2.3
+ - py-cpuinfo==9.0.0
+ - pygments==2.19.1
+ - pyparsing==3.2.3
+ - pytz==2025.2
+ - pyyaml==6.0.2
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diff --git a/output/digitization/test_digitized.csv b/output/digitization/test_digitized.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2b7ef7d385b402aea8e957d8778aa3964898ab17
--- /dev/null
+++ b/output/digitization/test_digitized.csv
@@ -0,0 +1,5220 @@
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diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1127350676d561f24132dea05b88bf03d210de3c
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,5 @@
+# utils/__init__.py
+"""
+Utility modules for the ECG Dashboard application.
+Provides hardware detection, branding/logo helpers, and CSS theming.
+"""
diff --git a/utils/branding.py b/utils/branding.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f6df114ad676566469e1b7ee0606230765b9046
--- /dev/null
+++ b/utils/branding.py
@@ -0,0 +1,69 @@
+# utils/branding.py
+"""
+Logo and branding helpers for the ECG Dashboard.
+Renders the SCAI Lab sidebar logo and the institutional footer logos.
+All logo file reads are cached so they only happen once.
+"""
+
+import os
+import base64
+import streamlit as st
+
+_ASSET_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets")
+
+
+@st.cache_data(show_spinner=False)
+def _load_logo_b64(filename: str) -> str | None:
+ """Load a logo file as base64 string. Cached per filename."""
+ filepath = os.path.join(_ASSET_DIR, filename)
+ if os.path.exists(filepath):
+ with open(filepath, "rb") as f:
+ return base64.b64encode(f.read()).decode()
+ return None
+
+
+def render_sidebar_logo():
+ """Render the SCAI Lab logo above the dashboard title in the sidebar."""
+ b64 = _load_logo_b64("scai_lab_logo.svg")
+ if b64:
+ st.sidebar.markdown(
+ f''
+ f'
'
+ f'
'
+ )
+
+ if logo_html_items:
+ logos_row = "".join(logo_html_items)
+ st.markdown("---")
+ st.markdown(
+ f''
+ f'{logos_row}
',
+ unsafe_allow_html=True
+ )
diff --git a/utils/css.py b/utils/css.py
new file mode 100644
index 0000000000000000000000000000000000000000..5607846ccbdca38b22d188a763f582bb06a33b2d
--- /dev/null
+++ b/utils/css.py
@@ -0,0 +1,155 @@
+# utils/css.py
+"""
+Centralized CSS theming for the ECG Dashboard.
+Injects the complete light clinical theme stylesheet.
+"""
+
+import streamlit as st
+
+
+def inject_custom_css():
+ """Inject the full custom CSS theme into the Streamlit page."""
+ st.markdown("""
+
+""", unsafe_allow_html=True)
diff --git a/utils/hardware.py b/utils/hardware.py
new file mode 100644
index 0000000000000000000000000000000000000000..60357050fda496dfa74b59200f4c0faeee2c98fa
--- /dev/null
+++ b/utils/hardware.py
@@ -0,0 +1,57 @@
+# utils/hardware.py
+"""
+Cached hardware detection for the ECG Dashboard sidebar.
+Uses @st.cache_data to avoid re-running expensive subprocess/torch calls on every Streamlit rerun.
+"""
+
+import platform
+import subprocess
+import streamlit as st
+
+
+@st.cache_data(show_spinner=False)
+def get_cpu_name() -> str:
+ """Detect CPU name. Cached so it only runs once per session."""
+ try:
+ return subprocess.check_output(
+ "wmic cpu get name", shell=True
+ ).decode().split("\n")[1].strip()
+ except Exception:
+ return platform.processor().split(',')[0].strip()
+
+
+@st.cache_data(show_spinner=False)
+def get_gpu_info() -> dict:
+ """
+ Detect GPU availability and details.
+ Cached so the heavy `import torch` only happens once per session.
+ Returns dict with keys: available, name, vram_gb.
+ """
+ try:
+ import torch
+ if torch.cuda.is_available():
+ return {
+ "available": True,
+ "name": torch.cuda.get_device_name(0),
+ "vram_gb": torch.cuda.get_device_properties(0).total_memory / 1e9
+ }
+ except ImportError:
+ pass
+ return {"available": False, "name": None, "vram_gb": None}
+
+
+def render_sidebar_hardware():
+ """Render the hardware info panel in the sidebar using cached data."""
+ st.sidebar.markdown("### ๐ Clinical Workstation Info")
+
+ cpu = get_cpu_name()
+ st.sidebar.markdown(f"**๐ฅ๏ธ CPU:**\n`{cpu}`")
+
+ gpu = get_gpu_info()
+ if gpu["available"]:
+ st.sidebar.markdown(
+ f"**๐ GPU:**\n`{gpu['name']}`\n"
+ f"`CUDA Active | VRAM: {gpu['vram_gb']:.1f} GB`"
+ )
+ else:
+ st.sidebar.markdown("**๐ GPU:**\n`Not Detected (Running on CPU)`")
diff --git a/utils/page_classifier.py b/utils/page_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7a74ef1f3f45b8390b306e8c081f322dc6acbd3
--- /dev/null
+++ b/utils/page_classifier.py
@@ -0,0 +1,397 @@
+# utils/page_classifier.py
+"""
+Page 3: ECG Classification.
+Handles CSV upload and pre-trained model inference.
+"""
+
+import os
+import tempfile
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import streamlit as st
+
+
+def render(config, classification_runner):
+ """Render the Classification page."""
+
+ # Page heading
+ st.markdown(
+ 'โค๏ธ ECG Classification
',
+ unsafe_allow_html=True
+ )
+ st.markdown(
+ ''
+ 'Run diagnostic classification tasks (MI vs Normal, OMI vs non-OMI, Pre vs Post-Procedural MI) '
+ 'using advanced pre-trained models.'
+ '
',
+ unsafe_allow_html=True
+ )
+
+ # Check dependencies before proceeding
+ dep_ok = True
+ try:
+ classification_runner.check_classification_dependencies()
+ except classification_runner.DependencyMissingError as e:
+ st.warning(str(e))
+ st.info(
+ "The application will show layout placeholders. "
+ "Install the packages in your terminal and restart/refresh to activate."
+ )
+ dep_ok = False
+
+ # 2-Column layout: Left panel (Controls), Right panel (Execution Steps & Results)
+ col1, col2 = st.columns([2, 3])
+
+ with col1:
+ # 1. Upload section
+ csv_to_use = _render_upload(config)
+
+ # 2. Configuration & run button
+ run_class_btn, selected_task, task_meta = _render_controls(config, classification_runner, csv_to_use, dep_ok)
+
+ with col2:
+ # Placeholder for real-time progress of running phases
+ status_placeholder = st.empty()
+
+ if run_class_btn:
+ if not csv_to_use:
+ st.error("Please upload a CSV dataset first.")
+ else:
+ _run_classification(config, classification_runner, csv_to_use, task_meta, selected_task, status_placeholder)
+
+ # 3. Show results in the right panel if classification is completed and matches selected task
+ if ("clf_results" in st.session_state and
+ st.session_state.get("selected_task") == selected_task):
+ _show_results(task_meta, selected_task)
+ elif not run_class_btn:
+ # Show a helpful guide card when idle
+ _show_idle_guide()
+
+
+def _render_upload(config):
+ """Render the CSV upload section. Returns the path to the CSV file or None.
+
+ Caches the temp file path in session state so we don't re-write the
+ same bytes to disk on every Streamlit rerun.
+ """
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ค Upload ECG Signals Dataset")
+
+ uploaded_csv = st.file_uploader(
+ "Select digitized ECG CSV dataset (multi-subject format with lead names as columns)",
+ type=["csv"],
+ key="classifier_csv_upload"
+ )
+ st.markdown('
', unsafe_allow_html=True)
+
+ csv_to_use = None
+
+ if uploaded_csv is not None:
+ # Only write to disk if this is a new / different file
+ prev_name = st.session_state.get("_clf_upload_name")
+ prev_path = st.session_state.get("_clf_upload_path")
+
+ if prev_name == uploaded_csv.name and prev_path and os.path.exists(prev_path):
+ csv_to_use = prev_path
+ else:
+ tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+ tfile.write(uploaded_csv.read())
+ tfile.close()
+ csv_to_use = tfile.name
+ st.session_state["_clf_upload_name"] = uploaded_csv.name
+ st.session_state["_clf_upload_path"] = csv_to_use
+ else:
+ # Clear cached path when file is removed
+ st.session_state.pop("_clf_upload_name", None)
+ st.session_state.pop("_clf_upload_path", None)
+
+ if csv_to_use:
+ _render_dataset_preview(csv_to_use)
+
+ return csv_to_use
+
+
+@st.cache_data(show_spinner="Loading dataset preview...")
+def _load_preview_data(csv_path: str, _mtime: float):
+ """Load the CSV once and return all data needed for the preview.
+
+ Cached on the file path + its mtime so it invalidates when the file changes.
+ Returns (head_df, row_count, leads_found, class_dist_or_none).
+ """
+ df = pd.read_csv(csv_path)
+ row_count = len(df)
+
+ leads_found = [col for col in ['I', 'II', 'III', 'aVR', 'aVL', 'aVF',
+ 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
+ if col in df.columns]
+
+ class_dist = None
+ if 'class' in df.columns:
+ class_dist = df['class'].value_counts().to_dict()
+
+ return df.head(10), row_count, leads_found, class_dist
+
+
+def _render_dataset_preview(csv_path):
+ """Show a preview of the loaded dataset โ single cached read instead of 3."""
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Dataset Preview")
+ try:
+ mtime = os.path.getmtime(csv_path)
+ head_df, row_count, leads_found, class_dist = _load_preview_data(csv_path, mtime)
+
+ st.dataframe(head_df, use_container_width=True)
+ st.markdown(
+ f"**Dataset Summary:**\n"
+ f"- **Total Rows**: `{row_count:,}` samples\n"
+ f"- **Available Leads**: `{leads_found}`"
+ )
+
+ if class_dist is not None:
+ st.markdown(f"- **Class Distribution**: `{class_dist}`")
+ except Exception as e:
+ st.error(f"Error parsing CSV file: {e}")
+ st.markdown('
', unsafe_allow_html=True)
+
+
+def _render_controls(config, classification_runner, csv_to_use, dep_ok):
+ """Render task selection and run button in left panel."""
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Task & Model Configuration")
+
+ task_options = list(config.CLASSIFICATION_TASKS.keys())
+ selected_task = st.selectbox(
+ "Select Diagnostic Classification Task",
+ options=task_options,
+ index=0
+ )
+
+ task_meta = config.CLASSIFICATION_TASKS[selected_task]
+ st.markdown(
+ f"
{task_meta['description']}
",
+ unsafe_allow_html=True
+ )
+
+ # Load pre-trained model metadata to display stats (now cached by the runner)
+ try:
+ _, metadata = classification_runner.load_pretrained_model_and_metadata(task_meta['model_dir'])
+ st.success(f"๐ฏ **Pre-trained model found!**")
+ st.markdown(
+ f"- **Model Class**: `{metadata['model_name']}`\n"
+ f"- **Expected Input**: `{metadata['n_features']} leads x {metadata['n_timesteps']} timesteps`\n"
+ f"- **Held-out Test Accuracy**: `{metadata['test_metrics']['accuracy']:.2%}`\n"
+ f"- **Training Date**: `{metadata['training_date']}`"
+ )
+ except Exception as e:
+ st.error(f"Could not load pre-trained model metadata: {e}")
+
+ run_class_btn = st.button(
+ "๐ฏ Run Diagnostic Classification",
+ use_container_width=True, disabled=not dep_ok
+ )
+
+ st.markdown('
', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Running Classification Pipeline...")
+ progress_bar = st.progress(0)
+ status_text = st.empty()
+ st.markdown('
', unsafe_allow_html=True)
+
+ logs = []
+ try:
+ status_text.markdown("๐ **Phase 1/3**: Loading pre-trained model weights...")
+ progress_bar.progress(20)
+ model, metadata = classification_runner.load_pretrained_model_and_metadata(task_meta['model_dir'])
+ logs.append(f"๐ฏ **Model Weights Loaded**: Loaded pre-trained `{metadata['model_name']}` for use-case `{metadata['use_case']}`.")
+
+ status_text.markdown("๐ **Phase 2/3**: Segmenting signals into heartbeats around R-peaks (Pan-Tompkins)...")
+ progress_bar.progress(50)
+ temp_segmented_csv = tempfile.NamedTemporaryFile(delete=False, suffix=".csv").name
+ segmented_df = classification_runner.segment_uploaded_csv(
+ csv_path, temp_segmented_csv, target_fs=config.TARGET_FS
+ )
+ n_beats = segmented_df.index.get_level_values('subject_id').nunique() if not segmented_df.empty else 0
+ logs.append(f"โก **Heartbeat Segmentation**: Segmented raw continuous signal into `{n_beats}` individual heartbeats around detected R-peaks (Pan-Tompkins).")
+
+ status_text.markdown("๐ **Phase 3/3**: Normalizing lead voltages & executing model inference...")
+ progress_bar.progress(80)
+ X, valid_ids, y_true = classification_runner.preprocess_dataframe_for_inference(segmented_df, metadata)
+ logs.append(f"๐ **Data Preprocessing**: Formatted data into Numpy3D tensor shape `{X.shape}` and applied max-absolute voltage normalization.")
+
+ results = classification_runner.run_pretrained_inference(model, X, metadata, valid_ids)
+ logs.append(f"๐ฎ **Inference Engine**: Evaluated model predictions and computed confidence levels for `{len(valid_ids)}` inputs.")
+
+ # Check if valid ground-truth labels are present
+ model_labels = [str(x).strip().lower() for x in metadata['class_labels']]
+ has_ground_truth = len(y_true) > 0 and any(str(lbl).strip().lower() in model_labels for lbl in y_true)
+
+ if has_ground_truth:
+ results['metrics'] = classification_runner.calculate_evaluation_metrics(
+ y_true, results['predicted_class'], metadata['class_labels'], metadata['positive_class']
+ )
+ logs.append("๐ **Evaluation Metrics**: Calculated performance metrics (Accuracy, F1, Sensitivity, Specificity) using available ground-truth labels.")
+ else:
+ results['metrics'] = None
+
+ progress_bar.progress(100)
+ status_text.success("โ
**Classification Pipeline Completed!**")
+
+ st.session_state["clf_results"] = results
+ st.session_state["clf_logs"] = logs
+ st.session_state["selected_task"] = selected_task
+
+ # Clear progress bar card since finished results are rendering
+ status_placeholder.empty()
+
+ except classification_runner.DependencyMissingError as e:
+ status_text.error(str(e))
+ except Exception as e:
+ status_text.error(f"โ Classification Pipeline Failed: {str(e)}")
+ st.exception(e)
+
+
+@st.cache_data(show_spinner=False)
+def _build_confusion_matrix_figure(cm_list, class_labels_mapped):
+ """Build and cache the confusion matrix matplotlib figure."""
+ fig_cm, ax_cm = plt.subplots(figsize=(4, 4))
+ cm_arr = np.array(cm_list)
+ ax_cm.imshow(cm_arr, interpolation='nearest', cmap=plt.cm.Reds)
+
+ ax_cm.set_xticks(range(len(class_labels_mapped)))
+ ax_cm.set_yticks(range(len(class_labels_mapped)))
+ ax_cm.set_xticklabels(class_labels_mapped, fontsize=8)
+ ax_cm.set_yticklabels(class_labels_mapped, fontsize=8)
+ ax_cm.set_xlabel("Predicted Label", fontsize=9)
+ ax_cm.set_ylabel("True Label", fontsize=9)
+
+ for r in range(cm_arr.shape[0]):
+ for c in range(cm_arr.shape[1]):
+ ax_cm.text(c, r, f"{cm_arr[r, c]}",
+ ha="center", va="center",
+ color="white" if cm_arr[r, c] > (cm_arr.max() / 2) else "black",
+ fontweight="bold")
+
+ fig_cm.patch.set_facecolor('white')
+ ax_cm.set_facecolor('white')
+ plt.tight_layout()
+ return fig_cm
+
+
+def _show_results(task_meta, selected_task):
+ """Display execution logs, performance metrics, confusion matrix, and predictions table."""
+ results = st.session_state["clf_results"]
+ logs = st.session_state.get("clf_logs", [])
+ metrics = results.get("metrics")
+
+ # 1. Pipeline Execution Status Logs
+ if logs:
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Pipeline Execution Logs")
+ for log in logs:
+ st.markdown(f"- {log}")
+ st.markdown('
', unsafe_allow_html=True)
+
+ # 2. Performance Metrics
+ if metrics is not None:
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Diagnostic Classification Performance Metrics")
+
+ st.markdown(f"""
+
+
+
Accuracy
+
{metrics['Accuracy']:.1%}
+
+
+
F1-Score
+
{metrics['F1']:.1%}
+
+
+
Sensitivity
+
{metrics['Sensitivity']:.1%}
+
+
+
Specificity
+
{metrics['Specificity']:.1%}
+
+
+ """, unsafe_allow_html=True)
+
+ col_cm1, col_cm2 = st.columns([1, 1])
+ with col_cm1:
+ st.markdown("**Confusion Matrix Counts:**")
+ st.markdown(
+ f"- **True Negatives (TN)**: `{metrics['TN']}`\n"
+ f"- **False Positives (FP)**: `{metrics['FP']}`\n"
+ f"- **False Negatives (FN)**: `{metrics['FN']}`\n"
+ f"- **True Positives (TP)**: `{metrics['TP']}`"
+ )
+ with col_cm2:
+ class_labels_mapped = list(task_meta["labels"].values())
+ fig_cm = _build_confusion_matrix_figure(
+ metrics["Confusion Matrix"], class_labels_mapped
+ )
+ st.pyplot(fig_cm)
+
+ st.markdown('
', unsafe_allow_html=True)
+ st.markdown("### ๐ Diagnostic Classification Predictions")
+ st.info("๐ก **Inference Mode:** The uploaded dataset does not contain ground-truth class labels. Diagnosis outputs for each subject/heartbeat are listed below.")
+ st.markdown('
', unsafe_allow_html=True)
+
+ # 3. Predictions Table
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Heartbeat Classification Predictions")
+
+ df_preds = pd.DataFrame({
+ "Subject/Heartbeat ID": results['subject_id'],
+ "Predicted Diagnosis": results['predicted_class']
+ })
+ if 'confidence' in results:
+ df_preds["Model Confidence"] = [f"{c:.1%}" for c in results['confidence']]
+
+ st.dataframe(df_preds, use_container_width=True)
+
+ # Download predictions CSV
+ preds_csv = df_preds.to_csv(index=False).encode('utf-8')
+ st.download_button(
+ label="๐ฅ Download Predictions Table (CSV)",
+ data=preds_csv,
+ file_name=f"{selected_task.replace(' ', '_').lower()}_predictions.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+ st.markdown('
', unsafe_allow_html=True)
+
+
+def _show_idle_guide():
+ """Display the instructions/guide in the right panel when idle."""
+ st.markdown("""
+
+
โค๏ธ Cardiac Diagnosis Workstation
+
+ This module applies pre-trained machine learning and deep learning models to classify cardiac conditions from digitized ECG voltage waveforms.
+
+
+ Standard Workstation Steps:
+
+
+ - Select a diagnostic task (e.g. OMI vs non-OMI) in the configuration panel on the left.
+ - Click ๐ฏ Run Diagnostic Classification to initiate the pipeline.
+ - The system will segment continuous signals into separate heartbeats around R-peaks.
+ - Inference will run instantly using pre-trained weights.
+
+
+ Upload a CSV in the configuration panel on the left to begin.
+
+
๐ ECG Signal Viewer
',
+ unsafe_allow_html=True
+ )
+ st.markdown(
+ ''
+ 'Upload any CSV file containing ECG signals or view the latest digitized signals using fully interactive charts.'
+ '
',
+ unsafe_allow_html=True
+ )
+
+ # --- Load Data Section ---
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Load Signal Data")
+
+ col_upload, col_settings = st.columns([2, 1])
+
+ with col_upload:
+ csv_file = st.file_uploader(
+ "Upload a CSV file with ECG signals",
+ type=["csv"],
+ key="csv_viewer_upload",
+ help="Each column should represent a lead or signal channel. Rows represent sequential samples."
+ )
+
+ with col_settings:
+ st.markdown("**Display Settings**")
+ sampling_rate = st.number_input(
+ "Sampling Rate (Hz)",
+ min_value=1,
+ max_value=10000,
+ value=500,
+ step=1,
+ help="Used to compute a time axis in seconds for the x-axis."
+ )
+
+ st.markdown('
', unsafe_allow_html=True)
+
+ # Load from uploaded file โ cached to avoid re-parsing on every rerun
+ df_signals = None
+ if csv_file is not None:
+ try:
+ raw_bytes = csv_file.getvalue()
+ df_signals = _parse_csv(raw_bytes)
+ except Exception as e:
+ st.error(f"โ Error reading uploaded CSV: {e}")
+
+ # --- Visualization & Summary ---
+ if df_signals is not None and not df_signals.empty:
+ numeric_cols = df_signals.select_dtypes(include=[np.number]).columns.tolist()
+ non_numeric = [c for c in df_signals.columns if c not in numeric_cols]
+
+ if not numeric_cols:
+ st.error("No numeric columns found in the CSV. ECG signals must be numeric data.")
+ else:
+ _render_summary(df_signals, numeric_cols, non_numeric, sampling_rate)
+ _render_plots(df_signals, numeric_cols, sampling_rate)
+ _render_trim_tool(df_signals, numeric_cols, sampling_rate)
+ _render_export(df_signals, numeric_cols)
+ else:
+ _show_guide()
+
+
+def _render_summary(df, numeric_cols, non_numeric, sampling_rate):
+ """Render dataset summary card."""
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Dataset Summary")
+
+ summary_cols = st.columns(4)
+ with summary_cols[0]:
+ st.metric("Total Rows", f"{len(df):,}")
+ with summary_cols[1]:
+ st.metric("Signal Channels", f"{len(numeric_cols)}")
+ with summary_cols[2]:
+ duration_s = len(df) / sampling_rate
+ st.metric("Duration", f"{duration_s:.2f} s")
+ with summary_cols[3]:
+ st.metric("Sampling Rate", f"{sampling_rate} Hz")
+
+ if non_numeric:
+ st.markdown(f"**Non-numeric columns** (excluded from visualization): `{non_numeric}`")
+
+ with st.expander("๐ Preview raw data (first 20 rows)"):
+ st.dataframe(df.head(20), use_container_width=True)
+
+ st.markdown('
', unsafe_allow_html=True)
+
+
+def _render_plots(df, numeric_cols, sampling_rate):
+ """Render the signal visualization section โ stacked subplots only (one chart per lead)."""
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ Interactive Signal Plots")
+ st.markdown(
+ "
"
+ "Zoom, pan, and hover over the interactive charts to inspect exact signal values. "
+ "Each lead is plotted in its own chart for clarity."
+ "
",
+ unsafe_allow_html=True
+ )
+
+ # Smart defaults: pick standard 12-lead names if present, else first 4
+ standard_leads = ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
+ default_selection = [c for c in standard_leads if c in numeric_cols]
+ if not default_selection:
+ default_selection = numeric_cols[:min(4, len(numeric_cols))]
+
+ selected_signals = st.multiselect(
+ "Select signals/leads to visualize",
+ options=numeric_cols,
+ default=default_selection
+ )
+
+ if selected_signals:
+ n_samples = len(df)
+ time_axis = np.arange(n_samples) / sampling_rate
+
+ # Build plot dataframe and downsample for large datasets
+ plot_df = df[selected_signals].copy()
+ plot_df.index = time_axis
+ plot_df.index.name = "Time (seconds)"
+
+ plot_df = _downsample_for_plot(plot_df, max_points=5000)
+
+ # Define 12-lead premium clinical color palette
+ lead_colors_map = {
+ 'I': '#E63946', # Clinical Red
+ 'II': '#0D9488', # Teal
+ 'III': '#1E3A8A', # Deep Blue
+ 'aVR': '#F59E0B', # Amber
+ 'aVL': '#8B5CF6', # Violet
+ 'aVF': '#EC4899', # Pink
+ 'V1': '#10B981', # Emerald
+ 'V2': '#3B82F6', # Blue
+ 'V3': '#6366F1', # Indigo
+ 'V4': '#F43F5E', # Rose
+ 'V5': '#84CC16', # Lime
+ 'V6': '#06B6D4' # Cyan
+ }
+ color_palette = [
+ '#E63946', '#0D9488', '#1E3A8A', '#F59E0B', '#8B5CF6', '#EC4899',
+ '#10B981', '#3B82F6', '#6366F1', '#F43F5E', '#84CC16', '#06B6D4'
+ ]
+
+ # Stacked layout: separate subplot per lead
+ for i, lead in enumerate(selected_signals):
+ st.markdown(f"**Lead {lead}**")
+ lead_color = lead_colors_map.get(lead, color_palette[i % len(color_palette)])
+ st.line_chart(plot_df[[lead]], color=lead_color, height=220, use_container_width=True)
+
+ # Signal statistics
+ with st.expander("๐ Signal Statistics"):
+ stats_df = df[selected_signals].describe().T
+ stats_df["range"] = stats_df["max"] - stats_df["min"]
+ st.dataframe(stats_df.style.format("{:.4f}"), use_container_width=True)
+ else:
+ st.info("Select one or more signal channels from the dropdown above to visualize.")
+
+ st.markdown('
', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Signal Trimming Tool")
+ st.markdown(
+ "
"
+ "Select a lead to preview, choose start and end points, then apply the trim to all leads and download the result."
+ "
",
+ unsafe_allow_html=True
+ )
+
+ total_samples = len(df)
+ max_time_s = total_samples / sampling_rate
+ # Floor-round to 2 decimals so the default value never exceeds max_value
+ max_time_s_rounded = float(np.floor(max_time_s * 100) / 100)
+
+ col_lead, col_start, col_end = st.columns([1, 1, 1])
+
+ with col_lead:
+ trim_lead = st.selectbox(
+ "Preview Lead",
+ options=numeric_cols,
+ index=0,
+ help="The lead shown in the preview chart below. The trim will be applied to ALL leads."
+ )
+
+ with col_start:
+ start_time = st.number_input(
+ "Start Time (s)",
+ min_value=0.0,
+ max_value=max_time_s_rounded,
+ value=0.0,
+ step=0.01,
+ format="%.2f",
+ help="Start of the region to keep."
+ )
+
+ with col_end:
+ end_time = st.number_input(
+ "End Time (s)",
+ min_value=0.0,
+ max_value=max_time_s_rounded,
+ value=max_time_s_rounded,
+ step=0.01,
+ format="%.2f",
+ help="End of the region to keep."
+ )
+
+ start_idx = int(start_time * sampling_rate)
+ end_idx = int(end_time * sampling_rate)
+
+ # Clamp indices
+ start_idx = max(0, min(start_idx, total_samples - 1))
+ end_idx = max(start_idx + 1, min(end_idx, total_samples))
+
+ trimmed_samples = end_idx - start_idx
+ trimmed_duration = trimmed_samples / sampling_rate
+
+ st.markdown(
+ f"**Trim range**: sample `{start_idx:,}` โ `{end_idx:,}` "
+ f"({trimmed_samples:,} samples, {trimmed_duration:.2f} s)"
+ )
+
+ # Preview the trimmed region for the selected lead
+ if trim_lead:
+ preview_data = df[trim_lead].iloc[start_idx:end_idx].values
+ preview_time = np.arange(len(preview_data)) / sampling_rate + start_time
+
+ preview_df = pd.DataFrame({trim_lead: preview_data}, index=preview_time)
+ preview_df.index.name = "Time (seconds)"
+
+ preview_df = _downsample_for_plot(preview_df, max_points=5000)
+
+ st.markdown(f"**Preview โ Lead {trim_lead} (trimmed)**")
+
+ lead_colors_map = {
+ 'I': '#E63946', 'II': '#0D9488', 'III': '#1E3A8A',
+ 'aVR': '#F59E0B', 'aVL': '#8B5CF6', 'aVF': '#EC4899',
+ 'V1': '#10B981', 'V2': '#3B82F6', 'V3': '#6366F1',
+ 'V4': '#F43F5E', 'V5': '#84CC16', 'V6': '#06B6D4'
+ }
+ preview_color = lead_colors_map.get(trim_lead, '#E63946')
+ st.line_chart(preview_df, color=preview_color, height=220, use_container_width=True)
+
+ # Action buttons
+ btn_col1, btn_col2 = st.columns(2)
+
+ with btn_col1:
+ # Download trimmed CSV
+ trimmed_df = df[numeric_cols].iloc[start_idx:end_idx]
+ trimmed_csv = _make_csv_bytes(trimmed_df.values, trimmed_df.columns.tolist())
+ st.download_button(
+ label="๐ฅ Download Trimmed CSV",
+ data=trimmed_csv,
+ file_name="ecg_trimmed.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+
+ with btn_col2:
+ # Download the full dataset (all columns, trimmed rows)
+ trimmed_full = df.iloc[start_idx:end_idx]
+ trimmed_full_csv = _make_csv_bytes(trimmed_full.values, trimmed_full.columns.tolist())
+ st.download_button(
+ label="๐ฅ Download Full Trimmed Dataset",
+ data=trimmed_full_csv,
+ file_name="ecg_trimmed_full.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+
+ st.markdown('
', unsafe_allow_html=True)
+ st.markdown("### ๐พ Export Options")
+
+ exp_col1, exp_col2 = st.columns(2)
+ with exp_col1:
+ csv_export = _make_csv_bytes(df[numeric_cols].values, list(numeric_cols))
+ st.download_button(
+ label="๐ฅ Download Numeric Signals (CSV)",
+ data=csv_export,
+ file_name="ecg_signals.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+ with exp_col2:
+ full_csv = _make_csv_bytes(df.values, list(df.columns))
+ st.download_button(
+ label="๐ฅ Download Full Dataset (CSV)",
+ data=full_csv,
+ file_name="ecg_full_dataset.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+ st.markdown('
', unsafe_allow_html=True)
+
+
+def _show_guide():
+ """Display the placeholder guide when no CSV is uploaded."""
+ st.markdown("""
+
+
๐ How to Use the ECG Signal Viewer
+
+ Upload any CSV file containing ECG signal data. The viewer supports:
+
+
+ - Multi-lead signals โ each column is treated as a separate signal channel
+ - Interactive plotting โ zoom, pan, hover, and overlay features via native canvas-based Streamlit plots
+ - Stacked subplots โ one chart per lead for clear side-by-side comparison
+ - Signal trimming โ select start/end times, preview the trimmed waveform, and download the result
+ - Adjustable sampling rate to compute the time axis in seconds
+ - Signal statistics including mean, std, min/max, and dynamic range
+
+
+ Supported format: CSV with numeric columns. Non-numeric columns (e.g., class labels, timestamps)
+ are automatically excluded from visualization.
+
+
๐ท ECG Image Digitizer
',
+ unsafe_allow_html=True
+ )
+ st.markdown(
+ ''
+ 'Convert printed/scanned 12-lead ECG paper reports into high-resolution digitized CSV voltage signals.'
+ '
',
+ unsafe_allow_html=True
+ )
+
+ # Check YOLO models at UI rendering
+ if not yolo_configured:
+ st.warning(
+ "โ ๏ธ **YOLO Detection & Segmentation Models are not configured!**\n\n"
+ "The model paths in `config.py` are currently empty string placeholders. "
+ "You can still preview the image and explore the dashboard layout. "
+ "To execute the digitization pipeline, please populate the model paths "
+ "in your local `config.py` file with the correct YOLO weights paths."
+ )
+
+ # Use a clean 2-column layout (Left: Controls & Outputs, Right: Preview or Guide)
+ col1, col2 = st.columns([2, 3])
+
+ image_to_use = None
+ image_name = ""
+
+ # Initialize last uploaded file tracking to maintain consistent state
+ if "last_uploaded_name" not in st.session_state:
+ st.session_state["last_uploaded_name"] = None
+
+ with col1:
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ค Upload ECG Scan")
+ uploaded_file = st.file_uploader(
+ "Select an ECG report image (PNG, JPG, JPEG)",
+ type=["png", "jpg", "jpeg"]
+ )
+ st.markdown('
', unsafe_allow_html=True)
+
+ if uploaded_file is not None:
+ # If a new file is uploaded, clear any stale state from previous runs
+ if st.session_state.get("last_uploaded_name") != uploaded_file.name:
+ st.session_state.pop("digitized_df", None)
+ st.session_state.pop("digitized_name", None)
+ st.session_state["last_uploaded_name"] = uploaded_file.name
+ # Write to a new temp file only when the upload changes
+ st.session_state.pop("_dig_upload_path", None)
+
+ # Cache the temp file path in session state so we don't re-write on every rerun
+ cached_path = st.session_state.get("_dig_upload_path")
+ if cached_path and os.path.exists(cached_path):
+ image_to_use = cached_path
+ else:
+ tfile = tempfile.NamedTemporaryFile(
+ delete=False, suffix=os.path.splitext(uploaded_file.name)[1]
+ )
+ tfile.write(uploaded_file.read())
+ tfile.close()
+ image_to_use = tfile.name
+ st.session_state["_dig_upload_path"] = image_to_use
+
+ image_name = os.path.splitext(uploaded_file.name)[0]
+ else:
+ # Clear state if file is removed
+ st.session_state.pop("digitized_df", None)
+ st.session_state.pop("digitized_name", None)
+ st.session_state.pop("_dig_upload_path", None)
+ st.session_state["last_uploaded_name"] = None
+
+ # Display results or trigger button
+ if "digitized_df" in st.session_state:
+ _show_results()
+ elif image_to_use:
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Digitization Control")
+ st.markdown(
+ "
"
+ "The image is ready. Click the button below to execute the digitization pipeline."
+ "
",
+ unsafe_allow_html=True
+ )
+ run_btn = st.button("โก Start Digitization Pipeline", use_container_width=True)
+ st.markdown('
', unsafe_allow_html=True)
+ st.markdown("### ๐ผ๏ธ ECG Report Preview")
+ st.image(image_to_use, use_container_width=True)
+ st.markdown('
', unsafe_allow_html=True)
+ else:
+ _show_guide()
+
+
+def _run_pipeline(config, digitization_runner, image_path, image_name):
+ """Execute the digitization pipeline with progress tracking."""
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### โ๏ธ Executing Digitization...")
+ progress_bar = st.progress(0)
+ status_text = st.empty()
+
+ try:
+ status_text.markdown("๐ **Stage 1/5**: Loading models & preprocessing image...")
+ progress_bar.progress(15)
+
+ digitization_runner.check_models_configured(config)
+ digitization_runner.check_model_files_exist(config)
+
+ status_text.markdown("๐ **Stage 2/5**: Executing lead segmentation & masking...")
+ progress_bar.progress(35)
+
+ status_text.markdown("๐ **Stage 3/5**: Running sequential YOLO detectors (Leads, names, pulses)...")
+ progress_bar.progress(55)
+
+ df_digitized, ecg_inst = digitization_runner.run_digitization_pipeline(image_path, config)
+
+ status_text.markdown("๐ **Stage 4/5**: Calibrating scale from reference pulses...")
+ progress_bar.progress(80)
+
+ status_text.markdown("๐ **Stage 5/5**: Building grid and extracting signals...")
+ progress_bar.progress(95)
+
+ progress_bar.progress(100)
+ status_text.success("โ
**Digitization completed successfully!**")
+
+ # Automatically save the digitized CSV to output directory
+ os.makedirs(config.DIGITIZATION_OUTPUT_DIR, exist_ok=True)
+
+ # Save both a named file and the shared "latest_digitized.csv" default file
+ named_path = os.path.join(config.DIGITIZATION_OUTPUT_DIR, f"{image_name}_digitized.csv")
+ default_path = os.path.join(config.DIGITIZATION_OUTPUT_DIR, "latest_digitized.csv")
+ df_digitized.to_csv(named_path, index=False)
+ df_digitized.to_csv(default_path, index=False)
+
+ st.session_state["digitized_df"] = df_digitized
+ st.session_state["digitized_name"] = image_name
+ st.rerun()
+
+ except digitization_runner.ModelPathNotSetError as e:
+ st.error(str(e))
+ except digitization_runner.ModelFileNotFoundError as e:
+ st.error(str(e))
+ except Exception as e:
+ st.error(f"โ Pipeline Failed: {str(e)}")
+ st.exception(e)
+ st.markdown('
', unsafe_allow_html=True)
+
+
+def _show_results():
+ """Display digitized signal results and export controls."""
+ df_res = st.session_state["digitized_df"]
+ name_res = st.session_state["digitized_name"]
+
+ st.markdown('', unsafe_allow_html=True)
+ st.markdown("### ๐ฅ Digitization Complete")
+ st.success("โ
**ECG signals extracted successfully!**")
+
+ # Summary details
+ st.markdown(f"**File Name:** `{name_res}`")
+ st.markdown(f"**Total Samples:** `{len(df_res):,}`")
+ st.markdown(f"**Detected Lead Channels ({len(df_res.columns)}):**")
+
+ # Format leads as inline badges/code elements
+ leads_str = " ".join([f"`{c}`" for c in df_res.columns])
+ st.markdown(leads_str)
+
+ st.markdown("
", unsafe_allow_html=True)
+
+ # Export controls
+ csv_data = df_res.to_csv(index=False).encode('utf-8')
+ st.download_button(
+ label="๐ฅ Download Digitized Signals (CSV)",
+ data=csv_data,
+ file_name=f"{name_res}_digitized.csv",
+ mime="text/csv",
+ use_container_width=True
+ )
+
+ st.markdown(
+ "
"
+ "๐ก **Pro-Tip:** Navigate to the **๐ CSV Signal Viewer** page using the sidebar menu and upload this downloaded CSV file to visualize, customize plots, overlay, and inspect the waves!"
+ "
",
+ unsafe_allow_html=True
+ )
+ st.markdown('
+
๐ฉบ Digital Electrocardiography Conversion
+
+ Load an ECG paper report scan in the panel on the left to start the digitization pipeline.
+ The deep learning models will automatically analyze the image to:
+
+
+ - Detect individual 12-lead regions using YOLO object detection.
+ - Perform precise signal waveform segmentation with YOLO patch models.
+ - Identify lead labels (I, II, aVR, V1-V6) with a text classifier.
+ - Locate reference calibration pulses to calculate scale constants.
+ - Reconstruct physical digitized voltage values calibrated in millivolts (mV).
+
+
+ Upload an ECG scan image to begin.
+
+
+
+
+
+ 
+
+
+
+ 
+
+
+
+ 
+
+