| import cv2 |
| import numpy as np |
| import pandas as pd |
| import gradio as gr |
| import matplotlib.pyplot as plt |
| from datetime import datetime |
|
|
| def preprocess_image(image): |
| """Enhance image contrast, apply thresholding, and clean noise.""" |
| if len(image.shape) == 2: |
| image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) |
|
|
| gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) |
| |
| |
| blurred = cv2.GaussianBlur(gray, (5, 5), 0) |
| |
| |
| _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) |
|
|
| |
| kernel = np.ones((3, 3), np.uint8) |
| clean_mask = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel, iterations=2) |
| clean_mask = cv2.morphologyEx(clean_mask, cv2.MORPH_OPEN, kernel, iterations=1) |
|
|
| return clean_mask |
|
|
| def detect_blood_cells(image): |
| """Detect blood cells and extract features.""" |
| mask = preprocess_image(image) |
| contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) |
|
|
| features = [] |
| total_area = 0 |
|
|
| for i, contour in enumerate(contours, 1): |
| area = cv2.contourArea(contour) |
| perimeter = cv2.arcLength(contour, True) |
| circularity = (4 * np.pi * area / (perimeter * perimeter)) if perimeter > 0 else 0 |
|
|
| |
| if 100 < area < 5000 and circularity > 0.7: |
| M = cv2.moments(contour) |
| if M["m00"] != 0: |
| cx = int(M["m10"] / M["m00"]) |
| cy = int(M["m01"] / M["m00"]) |
| features.append({ |
| 'ID': i, 'Area': area, 'Perimeter': perimeter, |
| 'Circularity': circularity, 'Centroid_X': cx, 'Centroid_Y': cy |
| }) |
| total_area += area |
|
|
| |
| avg_cell_size = total_area / len(features) if features else 0 |
| cell_density = len(features) / (image.shape[0] * image.shape[1]) |
|
|
| summary = { |
| 'Total Cells': len(features), |
| 'Avg Cell Size': avg_cell_size, |
| 'Cell Density': cell_density |
| } |
|
|
| return contours, features, mask, summary |
|
|
| def process_image(image): |
| if image is None: |
| return None, None, None, None, None |
|
|
| contours, features, mask, summary = detect_blood_cells(image) |
| vis_img = image.copy() |
|
|
| for feature in features: |
| contour = contours[feature['ID'] - 1] |
| cv2.drawContours(vis_img, [contour], -1, (0, 255, 0), 2) |
| cv2.putText(vis_img, str(feature['ID']), (feature['Centroid_X'], feature['Centroid_Y']), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) |
|
|
| df = pd.DataFrame(features) |
| return vis_img, mask, df, summary |
|
|
| def analyze(image): |
| vis_img, mask, df, summary = process_image(image) |
|
|
| plt.style.use('dark_background') |
| fig, axes = plt.subplots(1, 2, figsize=(12, 5)) |
|
|
| if not df.empty: |
| axes[0].hist(df['Area'], bins=20, color='cyan', edgecolor='black') |
| axes[0].set_title('Cell Size Distribution') |
|
|
| axes[1].scatter(df['Area'], df['Circularity'], alpha=0.6, c='magenta') |
| axes[1].set_title('Area vs Circularity') |
|
|
| return vis_img, mask, fig, df, summary |
|
|
| |
| demo = gr.Interface( |
| fn=analyze, |
| inputs=gr.Image(type="numpy"), |
| outputs=[ |
| gr.Image(label="Processed Image"), |
| gr.Image(label="Binary Mask"), |
| gr.Plot(label="Analysis Plots"), |
| gr.Dataframe(label="Detected Cells Data"), |
| gr.JSON(label="Summary Statistics") |
| ] |
| ) |
|
|
| demo.launch() |
|
|
