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import cv2
import numpy as np
import pandas as pd
import gradio as gr
from skimage import measure, morphology
from skimage.segmentation import watershed
import matplotlib.pyplot as plt
def process_image(image):
"""Process uploaded image and extract cell features"""
# Convert to BGR if image is RGB
if len(image.shape) == 3:
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Basic preprocessing
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
enhanced = clahe.apply(gray)
blurred = cv2.medianBlur(enhanced, 5)
# Segmentation
thresh = cv2.adaptiveThreshold(
blurred, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 21, 4
)
# Clean small noise
cleaned = morphology.opening(thresh, morphology.disk(2))
# Watershed segmentation
sure_bg = cv2.dilate(cleaned, morphology.disk(3), iterations=3)
dist = cv2.distanceTransform(cleaned, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist, 0.5*dist.max(), 255, 0)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
markers += 1
markers[unknown == 255] = 0
markers = watershed(-dist, markers, mask=cleaned)
# Extract features
features = []
props = measure.regionprops(markers, intensity_image=gray)
for i, prop in enumerate(props):
if prop.area < 50: # Filter small regions
continue
features.append({
'cell_id': i+1,
'area': prop.area,
'perimeter': prop.perimeter,
'circularity': (4 * np.pi * prop.area) / (prop.perimeter**2 + 1e-6),
'mean_intensity': prop.mean_intensity,
'centroid_x': prop.centroid[1],
'centroid_y': prop.centroid[0]
})
# Create visualization
vis_img = image.copy()
contours = measure.find_contours(markers, 0.5)
# Draw contours and cell IDs
for contour in contours:
coords = contour.astype(int)
cv2.drawContours(vis_img, [coords], -1, (0,255,0), 1)
for region in measure.regionprops(markers):
if region.area >= 50:
y, x = region.centroid
cv2.putText(vis_img, str(region.label),
(int(x), int(y)),
cv2.FONT_HERSHEY_SIMPLEX,
0.4, (255,0,0), 1)
# Create summary plots
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
# Cell size distribution
df = pd.DataFrame(features)
df['area'].hist(ax=axes[0], bins=20)
axes[0].set_title('Cell Size Distribution')
axes[0].set_xlabel('Area')
axes[0].set_ylabel('Count')
# Circularity vs Intensity
axes[1].scatter(df['circularity'], df['mean_intensity'])
axes[1].set_title('Circularity vs Intensity')
axes[1].set_xlabel('Circularity')
axes[1].set_ylabel('Mean Intensity')
plt.tight_layout()
return (
cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB),
fig,
df
)
# Create Gradio interface
with gr.Blocks(title="Cell Analysis Tool") as demo:
gr.Markdown("""
# 🔬 Bioengineering Cell Analysis Tool
Upload microscopy images to analyze cell properties:
- Automated cell detection
- Feature extraction (size, shape, intensity)
- Statistical analysis
**Instructions:**
1. Upload an image containing cells
2. Wait for analysis to complete
3. Review results in three tabs:
- Detected cells visualization
- Statistical plots
- Detailed measurements table
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Upload Image",
type="numpy",
tool="upload"
)
analyze_btn = gr.Button(
"Analyze Image",
variant="primary"
)
with gr.Column():
with gr.Tabs():
with gr.Tab("Detection Results"):
output_image = gr.Image(
label="Detected Cells"
)
with gr.Tab("Statistics"):
output_plot = gr.Plot(
label="Statistical Analysis"
)
with gr.Tab("Measurements"):
output_table = gr.DataFrame(
label="Cell Features"
)
analyze_btn.click(
fn=process_image,
inputs=input_image,
outputs=[output_image, output_plot, output_table]
)
# Launch the demo
if __name__ == "__main__":
demo.launch() |