Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+def process_image(image, calibration_factor):
+    """
+    Processes the input image to measure the hair diameter.
+    
+    Args:
+        image (numpy array): Uploaded image in RGB format.
+        calibration_factor (float): Conversion factor from pixels to micrometers.
+    
+    Returns:
+        tuple: A result text and the annotated image.
+    """
+    if image is None:
+        return "No image provided.", None
+
+    # Convert image from RGB (Gradio default) to BGR (OpenCV default)
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    # Convert to grayscale
+    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
+    
+    # Apply Gaussian blur to reduce noise
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    
+    # Apply thresholding (using Otsu's method) to create a binary image
+    ret, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    
+    # Find contours in the thresholded image
+    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    if not contours:
+        return "No hair contour found.", image
+
+    # Assume the hair strand is the largest contour
+    hair_contour = max(contours, key=cv2.contourArea)
+    
+    # Fit a minimum area rectangle to the contour
+    rect = cv2.minAreaRect(hair_contour)
+    (center, (width, height), angle) = rect
+    
+    # The smaller dimension is considered the hair's diameter in pixels
+    diameter_pixels = min(width, height)
+    diameter_real = diameter_pixels * calibration_factor  # Convert to micrometers
+    
+    # Draw the fitted rectangle on the image for visualization
+    box = cv2.boxPoints(rect)
+    box = np.int0(box)
+    annotated = image_bgr.copy()
+    cv2.drawContours(annotated, [box], 0, (0, 255, 0), 2)
+    
+    # Convert annotated image back to RGB for Gradio display
+    annotated_rgb = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
+    
+    result_text = f"Estimated Hair Diameter: {diameter_real:.2f} micrometers"
+    return result_text, annotated_rgb
+
+# Build the Gradio interface
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Hair Image"),
+        gr.Number(value=0.5, label="Calibration Factor (micrometers per pixel)")
+    ],
+    outputs=[
+        gr.Textbox(label="Result"),
+        gr.Image(label="Annotated Image")
+    ],
+    title="Hair Diameter Measurement Tool",
+    description=(
+        "Upload a high-resolution image of a hair sample (captured with a dermascope) "
+        "and provide a calibration factor based on a reference object included in the image. "
+        "This tool processes the image to estimate the hair diameter using image processing techniques."
+    )
+)
+
+if __name__ == '__main__':
+    iface.launch()