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app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+def image_inference(image_path, mode, epsilon_thresh):
+    # Read the image
+    img = cv2.cvtColor(image_path, cv2.COLOR_RGB2BGR)
+    if img is None:
+        raise FileNotFoundError(f"Image at path '{image_path}' not found.")
+    
+    # Create a copy of the original image
+    img_copy = img.copy()
+    
+    # Convert the image to grayscale
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Apply thresholding
+    _, img_thresh = cv2.threshold(img_gray, 200, 255, cv2.THRESH_BINARY_INV)
+
+    # Find the contours
+    contours, _ = cv2.findContours(img_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    contour_info = f"Number of contours found: {len(contours)}"  # Text output for Gradio
+    # print(f"Number of contours found = {len(contours)}")
+    
+    def convert_color(hsv):
+        # Utility to convert a single HSV color tuple into BGR
+        pixel_img = np.uint8([[hsv]])
+        return tuple(int(i) for i in cv2.cvtColor(pixel_img, cv2.COLOR_HSV2BGR).flatten())
+
+    if mode == "contour":
+        if len(contours) > 0:
+            for i, single_contour in enumerate(contours):
+                hsv = (int(i/len(contours) * 180), 255, 255)
+                color = convert_color(hsv)
+                # Draw the contour
+                img_final = cv2.drawContours(img_copy, contours, i, color, 4)
+                # Calculate and display the area
+                # area = cv2.contourArea(single_contour)
+                # x, y, w, h = cv2.boundingRect(single_contour)
+                # img_final = cv2.putText(img_final, f"Area: {area}", (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
+        else:
+            img_final = img_copy  # No contours found, return the original image
+    elif mode == "rotated rectangle":
+        for cnt in contours:
+            # Rotated bounding box
+            box = cv2.minAreaRect(cnt)
+            box_pts = np.intp(cv2.boxPoints(box))
+            img_final = cv2.drawContours(img_copy, [box_pts], -1, (0, 255, 0), 4)
+            
+            # Calculate and display the area
+            area = cv2.contourArea(cnt)
+            x, y, w, h = cv2.boundingRect(cnt)
+            img_final = cv2.putText(img_final, f"Area: {area}", (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
+            
+    elif mode == "rectangle":
+        for cnt in contours:
+            # Bounding rectangle
+            x, y, w, h = cv2.boundingRect(cnt)
+            img_final = cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 4)
+            
+            # Calculate and display the area
+            area = cv2.contourArea(cnt)
+            img_final = cv2.putText(img_final, f"Area: {area}", (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
+            
+    elif mode == "circle":
+        for cnt in contours:
+            # Enclosing circle
+            ((x, y), radius) = cv2.minEnclosingCircle(cnt)
+            img_final = cv2.circle(img_copy, (int(x), int(y)), int(round(radius)), (0, 255, 0), 4)
+            
+            # Calculate and display the perimeter
+            perimeter = cv2.arcLength(cnt, True)
+            x, y, w, h = cv2.boundingRect(cnt)
+            img_final = cv2.putText(img_final, f"Perimeter: {perimeter:.2f}", (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
+            
+    elif mode == "ellipse":
+        for cnt in contours:
+            if len(cnt) >= 5:  # fitEllipse requires at least 5 points
+                ellipse = cv2.fitEllipse(cnt)
+                img_final = cv2.ellipse(img_copy, ellipse, (0, 255, 0), 4)
+                
+                # Calculate and display the perimeter
+                perimeter = cv2.arcLength(cnt, True)
+                x, y, w, h = cv2.boundingRect(cnt)
+                img_final = cv2.putText(img_final, f"Perimeter: {perimeter:.2f}", (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
+            else:
+                img_final = img_copy
+    elif mode == "centroid":
+        for cnt in contours:
+            M = cv2.moments(cnt)
+            if M["m00"] != 0:
+                x = int(round(M["m10"] / M["m00"]))
+                y = int(round(M["m01"] / M["m00"]))
+                img_final = cv2.circle(img_copy, (x, y), 10, (255, 0, 0), -1)
+            else:
+                img_final = img_copy
+    elif mode == "contour approx":
+        if len(contours) > 0:
+            c = max(contours, key=cv2.contourArea)
+            peri = cv2.arcLength(c, True)
+            approx = cv2.approxPolyDP(c, epsilon_thresh * peri, True)
+            img_final = cv2.drawContours(img_copy, [approx], -1, (0, 255, 0), 3)
+            x, y, w, h = cv2.boundingRect(c)
+            text = f"eps={epsilon_thresh:.4f}, num_pts={len(approx)}"
+            img_final = cv2.putText(img_copy, text, (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        else:
+            img_final = img_copy
+    else:
+        img_final = img_copy
+        print(f"Mode '{mode}' not recognized. Please choose a valid mode.")
+    
+    return contour_info, cv2.cvtColor(img_final, cv2.COLOR_BGR2RGB)
+
+# Gradio interface
+input_image = gr.Image(type="numpy", label="Input Image")
+epsilon_thresh = gr.Slider(
+    0.01,
+    0.1,
+    step=0.01,
+    value=0.05,
+    label="Epsilon Threshold",
+    info="Adjust the Contour Threshold according to the object size that you want to detect. Only Applicable for Contour Approx",
+)
+mode = gr.Radio(
+    ["contour", "rectangle", "rotated rectangle", "circle", "ellipse", "centroid", "contour approx"],
+    label="Contour Type",
+    info="Choose the MODE",
+)
+output_text = gr.Textbox(label="Contour Information")
+output_image = gr.Image(label="Output Image")
+
+app = gr.Interface(
+    fn=image_inference,
+    inputs=[input_image, mode, epsilon_thresh],
+    outputs=[output_text, output_image],
+    title="Contour Detection using OpenCV",
+    description="A Gradio app for dynamic image analysis using Contour detection techniques.",
+    allow_flagging="never",
+    examples=[["./sample/tictactoe.png", "contour", float(0.01)], ["./sample/tetris_blocks.png", "rectangle", float(0.00)], ["./sample/shape.png", "contour approx", float(0.05)]],
+    cache_examples=False,
+)
+app.queue().launch()

requirements.txt

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+gradio==4.42.0
+opencv-python==4.10.0.84