| --- |
| license: apache-2.0 |
| task_categories: |
| - object-detection |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| pretty_name: Coco |
| --- |
| |
| # Coco dataset loader based on tensorflow dataset coco |
|
|
| ## Object Detection |
|
|
| ```python |
| import os |
| from datasets import load_dataset |
| from PIL import Image, ImageFont, ImageDraw, ImageColor |
| |
| def calc_lum(rgb): |
| return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2]) |
| |
| COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()] |
| |
| def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"): |
| m = margin |
| l, t, r, b = bb |
| tl, tt, tr, tb = tbb |
| bbw, bbh = r - l, b - t |
| tbbw, tbbh = tr - tl, tb - tt |
| |
| # bbox (left-top) |
| if anchor == "leftTop": |
| ax, ay = l, t |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-bottom) |
| x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0) |
| x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-top) |
| x1, y1 = max(ax, 0), max(ay, 0) |
| x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h) |
| return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "rightTop": |
| ax, ay = r, t |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-bottom) |
| x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0) |
| x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-top) |
| x2, y1 = max(ax, 0), max(ay, 0) |
| x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "rightBottom": |
| ax, ay = r, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h) |
| x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x2, y2 = min(ax, im_w), max(ay, 0) |
| x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "leftBottom": |
| ax, ay = l, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x1, y2 = min(ax, im_w), max(ay, 0) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "centerBottom": |
| ax, ay = (l+r)//2, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| |
| def draw_bbox(image, objects, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, fill=True, opacity=60, width=2, margin=3, anchor="leftBottom"): |
| fnt = ImageFont.truetype(font, fontsize) |
| im_w, im_h = image.size |
| |
| |
| img = image.convert("RGBA") |
| overlay = Image.new('RGBA', img.size, (0, 0, 0, 0)) |
| draw = ImageDraw.Draw(overlay) |
| for bb, lbl_id in zip(objects["bbox"], objects["label"]): |
| c = COLOR_MAP[min(lbl_id, len(COLOR_MAP)-1)] |
| fill_c = c + (opacity, ) if fill else None |
| draw.rectangle((bb[0], bb[1], bb[2], bb[3]), outline=c, fill=fill_c, width=width) |
| |
| text = "" |
| if label_names is not None: |
| text = label_names[lbl_id] |
| tbb = fnt.getbbox(text) |
| btn_bbox, text_pos = get_text_bbox(bb, tbb, margin, im_w, im_h, anchor) |
| fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255) |
| draw.rectangle(btn_bbox, outline=c, fill=c + (255, )) |
| draw.text(text_pos, text, font=fnt, fill=fc + (255, )) |
| |
| img = Image.alpha_composite(img, overlay) |
| overlay = Image.new('RGBA', img.size, (0, 0, 0, 0)) |
| draw = ImageDraw.Draw(overlay) |
| img = img.convert("RGB") |
| img.save(out_path) |
| |
| raw_datasets = load_dataset( |
| "coco.py", |
| "2017", |
| cache_dir="./huggingface_datasets", |
| ) |
| |
| train_dataset = raw_datasets["train"] |
| label_list = raw_datasets["train"].features["objects"].feature['label'].names |
| |
| for idx, item in zip(range(10), train_dataset): |
| draw_bbox(item["image"], item["objects"], item["image/filename"], label_list) |
| |
| ``` |
|
|
|  |
|  |
|
|
|
|
| ## Panoptic segmentation |
|
|
| ```python |
| |
| import numpy as np |
| from datasets import load_dataset |
| from PIL import Image, ImageFont, ImageDraw, ImageColor |
| from transformers.image_transforms import ( |
| rgb_to_id, |
| ) |
| |
| def calc_lum(rgb): |
| return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2]) |
| |
| COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()] |
| |
| def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"): |
| m = margin |
| l, t, r, b = bb |
| tl, tt, tr, tb = tbb |
| bbw, bbh = r - l, b - t |
| tbbw, tbbh = tr - tl, tb - tt |
| |
| # bbox (left-top) |
| if anchor == "leftTop": |
| ax, ay = l, t |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-bottom) |
| x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0) |
| x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-top) |
| x1, y1 = max(ax, 0), max(ay, 0) |
| x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h) |
| return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "rightTop": |
| ax, ay = r, t |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-bottom) |
| x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0) |
| x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-top) |
| x2, y1 = max(ax, 0), max(ay, 0) |
| x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "rightBottom": |
| ax, ay = r, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h) |
| x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x2, y2 = min(ax, im_w), max(ay, 0) |
| x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "leftBottom": |
| ax, ay = l, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x1, y2 = min(ax, im_w), max(ay, 0) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| elif anchor == "centerBottom": |
| ax, ay = (l+r)//2, b |
| if tbbw*3 > bbw or tbbh*4 > bbh: |
| # align (text box: left-top) |
| x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| else: |
| # align (text box: left-bottom) |
| x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0) |
| x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0) |
| return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0))) |
| |
| # Copied from transformers.models.detr.image_processing_detr.masks_to_boxes |
| def masks_to_boxes(masks: np.ndarray) -> np.ndarray: |
| """ |
| Compute the bounding boxes around the provided panoptic segmentation masks. |
| Args: |
| masks: masks in format `[number_masks, height, width]` where N is the number of masks |
| Returns: |
| boxes: bounding boxes in format `[number_masks, 4]` in xyxy format |
| """ |
| if masks.size == 0: |
| return np.zeros((0, 4)) |
| |
| h, w = masks.shape[-2:] |
| y = np.arange(0, h, dtype=np.float32) |
| x = np.arange(0, w, dtype=np.float32) |
| # see https://github.com/pytorch/pytorch/issues/50276 |
| y, x = np.meshgrid(y, x, indexing="ij") |
| |
| x_mask = masks * np.expand_dims(x, axis=0) |
| x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1) |
| x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool))) |
| x_min = x.filled(fill_value=1e8) |
| x_min = x_min.reshape(x_min.shape[0], -1).min(-1) |
| |
| y_mask = masks * np.expand_dims(y, axis=0) |
| y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1) |
| y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool))) |
| y_min = y.filled(fill_value=1e8) |
| y_min = y_min.reshape(y_min.shape[0], -1).min(-1) |
| |
| return np.stack([x_min, y_min, x_max, y_max], 1) |
| |
| def draw_seg(image, panoptic_image, oids, labels, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, opacity=160, anchor="leftBottom"): |
| fnt = ImageFont.truetype(font, fontsize) |
| im_w, im_h = image.size |
| |
| masks = np.asarray(panoptic_image, dtype=np.uint32) |
| masks = rgb_to_id(masks) |
| |
| oids = np.array(oids, dtype=np.uint32) |
| masks = masks == oids[:, None, None] |
| masks = masks.astype(np.uint8) |
| |
| bboxes = masks_to_boxes(masks) |
| |
| img = image.convert("RGBA") |
| |
| for label, mask, bbox in zip(labels, masks, bboxes): |
| c = COLOR_MAP[min(label, len(COLOR_MAP)-1)] |
| cf = np.array(c + (opacity, )).astype(np.uint8) |
| cmask = mask[:, :, None] * cf[None, None, :] |
| cmask = Image.fromarray(cmask) |
| img = Image.alpha_composite(img, cmask) |
| |
| if label_names is not None: |
| text = label_names[label] |
| tbb = fnt.getbbox(text) |
| btn_bbox, text_pos = get_text_bbox(bbox, tbb, 3, im_w, im_h, anchor=anchor) |
| |
| overlay = Image.new('RGBA', img.size, (0, 0, 0, 0)) |
| draw = ImageDraw.Draw(overlay) |
| |
| fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255) |
| |
| draw.rectangle(btn_bbox, outline=c, fill=c + (255, )) |
| draw.text(text_pos, text, font=fnt, fill=fc + (255, )) |
| |
| img = Image.alpha_composite(img, overlay) |
| |
| img = img.convert("RGB") |
| img.save(out_path) |
| |
| |
| |
| raw_datasets = load_dataset( |
| "coco.py", |
| "2017_panoptic", |
| cache_dir="./huggingface_datasets", |
| # data_dir="./data", |
| ) |
| |
| train_dataset = raw_datasets["train"] |
| label_list = raw_datasets["train"].features["panoptic_objects"].feature['label'].names |
| |
| for idx, item in zip(range(10), train_dataset): |
| draw_seg( |
| item["image"], |
| item["panoptic_image"], |
| item["panoptic_objects"]["id"], |
| item["panoptic_objects"]["label"], |
| "panoptic_" + item["image/filename"], |
| label_list) |
| |
| ``` |
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