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detection_on_image.py
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detection_on_image.py
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import cv2
import numpy as np
import matplotlib.pyplot as plt
# Step 1) Load image
img = cv2.imread("./Data/test_img.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Step 2) Gray Scale
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray_img = cv2.dilate(gray_img, kernel=np.ones((5, 5), np.uint8))
# Step 3) Canny
canny = cv2.Canny(gray_img, 100, 200)
# Step 4) define ROI Vertices
roi_vertices = [(270, 670), (600, 400), (1127, 712)]
# Step 5) define ROI function
def roi(image, vertices):
mask = np.zeros_like(image)
mask_color = 255
cv2.fillPoly(mask, vertices, mask_color)
masked_img = cv2.bitwise_and(image, mask)
return masked_img
# Step 6) ROI Image
roi_image = roi(canny, np.array([roi_vertices], np.int32))
# Step 7) Apply Hough Lines P Method on ROI Image
lines = cv2.HoughLinesP(roi_image, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)
# Step 8) Draw Hough lines
def draw_lines(image, hough_lines):
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
return image
final_img = draw_lines(img, lines) # Result
plt.imshow(final_img)
plt.xticks([])
plt.yticks([])
plt.show()