line detection

app.py

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+import gradio as gr
+from utils import (
+    sobel_edge_detection,
+    canny_edge_detection,
+    hough_lines,
+    laplacian_edge_detection,
+    contours_detection,
+    prewitt_edge_detection,
+    gradient_magnitude,
+    corner_detection,
+)
+
+def predict_image( input_image,algorithm):
+    algorithm_functions = {
+        "Sobel Edge Detection": sobel_edge_detection,
+        "Canny Edge Detection": canny_edge_detection,
+        "Hough Lines": hough_lines,
+        "Laplacian Edge Detection": laplacian_edge_detection,
+        "Contours Detection": contours_detection,
+        "Prewitt Edge Detection": prewitt_edge_detection,
+        "Gradient Magnitude": gradient_magnitude,
+        "Corner Detection": corner_detection,
+    }
+    
+    # Apply the selected image processing algorithm
+    if algorithm in algorithm_functions:
+        processed_image = algorithm_functions[algorithm](input_image)
+    else:
+        processed_image = input_image  # Default to original image if algorithm not found
+
+    return processed_image
+
+GrImage = gr.Image()
+GrDropdown = gr.Dropdown(
+    [
+        "Sobel Edge Detection",
+        "Canny Edge Detection",
+        "Hough Lines",
+        "Laplacian Edge Detection",
+        "Contours Detection",
+        "Prewitt Edge Detection",
+        "Gradient Magnitude",
+        "Corner Detection",
+    ]
+)
+
+iface = gr.Interface(fn=predict_image, inputs=[GrImage, GrDropdown], outputs="image")
+iface.launch()

flagged/log.csv

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+input_image,output,flag,username,timestamp
+C:\Users\Myo Win Zaw\Desktop\2k3\ai\day4\line-detection\flagged\input_image\497a84a343b3f1e548bca2c7a19db9a72960b0fd\tmpluaqy_ap.png,C:\Users\Myo Win Zaw\Desktop\2k3\ai\day4\line-detection\flagged\output\6e4d7bc7046277291635b69cd1c9655fe13a7564\tmptlbwx7ok.png,,,2023-12-23 16:39:29.327812

requirements.txt

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+gradio
+numpy
+opencv-python

test.py

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+import gradio as gr
+from utils import (
+    sobel_edge_detection,
+    canny_edge_detection,
+    hough_lines,
+    laplacian_edge_detection,
+    contours_detection,
+    prewitt_edge_detection,
+    gradient_magnitude,
+    corner_detection,
+)
+import cv2
+import numpy as np
+
+
+def predict_image(algorithm, image):
+    # Apply edge detection (e.g., Canny)
+    edges = cv2.Canny(image, 50, 150, apertureSize=3)
+
+    # Apply Hough Line Transform
+    lines = cv2.HoughLines(edges, 1, np.pi / 180, threshold=100)
+
+    # Draw lines on the original image
+    for line in lines:
+        rho, theta = line[0]
+        a = np.cos(theta)
+        b = np.sin(theta)
+        x0 = a * rho
+        y0 = b * rho
+        x1 = int(x0 + 1000 * (-b))
+        y1 = int(y0 + 1000 * (a))
+        x2 = int(x0 - 1000 * (-b))
+        y2 = int(y0 - 1000 * (a))
+        cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
+    
+    return edges
+
+GrImage = gr.Image()
+GrDropdown = gr.Dropdown(
+    [
+        "Sobel Edge Detection",
+        "Canny Edge Detection",
+        "Hough Lines",
+        "Laplacian Edge Detection",
+        "Contours Detection",
+        "Prewitt Edge Detection",
+        "Gradient Magnitude",
+        "Corner Detection",
+    ]
+)
+
+GrOutput = gr.Image()
+
+iface = gr.Interface(fn=predict_image, inputs=[ GrDropdown,GrImage], outputs=GrOutput)
+iface.launch()

utils.py

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+import cv2
+import numpy as np
+
+def sobel_edge_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
+    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
+    magnitude = np.sqrt(sobelx**2 + sobely**2)
+    magnitude = np.uint8(magnitude)
+    return magnitude
+
+def canny_edge_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
+    return edges
+
+def hough_lines(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150)
+    lines = cv2.HoughLines(edges, 1, np.pi / 180, threshold=100)
+    result = image.copy()
+    for line in lines:
+        rho, theta = line[0]
+        a = np.cos(theta)
+        b = np.sin(theta)
+        x0 = a * rho
+        y0 = b * rho
+        x1 = int(x0 + 1000 * (-b))
+        y1 = int(y0 + 1000 * (a))
+        x2 = int(x0 - 1000 * (-b))
+        y2 = int(y0 - 1000 * (a))
+        cv2.line(result, (x1, y1), (x2, y2), (0, 0, 255), 2)
+    return result
+
+def laplacian_edge_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
+    laplacian = np.uint8(np.absolute(laplacian))
+    return laplacian
+
+def contours_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    result = np.zeros_like(image)
+    cv2.drawContours(result, contours, -1, (0, 255, 0), 2)
+    return result
+
+def prewitt_edge_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    prewittx = cv2.filter2D(gray, cv2.CV_64F, np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]))
+    prewitty = cv2.filter2D(gray, cv2.CV_64F, np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]))
+    magnitude = np.sqrt(prewittx**2 + prewitty**2)
+    magnitude = np.uint8(magnitude)
+    return magnitude
+
+def gradient_magnitude(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
+    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
+    magnitude = np.sqrt(sobelx**2 + sobely**2)
+    magnitude = np.uint8(magnitude)
+    return magnitude
+
+def corner_detection(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    corners = cv2.goodFeaturesToTrack(gray, maxCorners=100, qualityLevel=0.01, minDistance=10)
+    result = np.zeros_like(image)
+    corners = np.int0(corners)
+    for i in corners:
+        x, y = i.ravel()
+        cv2.circle(result, (x, y), 3, 255, -1)
+    return result