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hough变换检测线条变化.py

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+import cv2
+import numpy as np
+
+img = cv2.imread("da.jpg")
+
+img = cv2.GaussianBlur(img, (3, 3), 0)
+edges = cv2.Canny(img, 50, 150, apertureSize=3)
+
+
+# 使用HoughLines算法
+# rho为1
+# theta为np.pi/2
+# threshold为 118
+# 其他的默认
+
+lines = cv2.HoughLines(edges, rho=1, theta=np.pi/2, threshold=118)
+
+
+result = img.copy()
+for i_line in lines:
+    for line in i_line:
+        rho = line[0]
+        theta = line[1]
+        if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)):  # 垂直直线
+            pt1 = (int(rho / np.cos(theta)), 0)
+            pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
+            cv2.line(result, pt1, pt2, (0, 0, 255))
+        else:
+            pt1 = (0, int(rho / np.sin(theta)))
+            pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
+            cv2.line(result, pt1, pt2, (0, 0, 255), 1)
+
+
+minLineLength = 200
+maxLineGap = 15
+
+
+# 使用HoughLinesP算法
+# rho为1
+# theta为np.pi/2
+# threshold为 118
+# 并设置上面提供的minLineLength和maxLineGap
+# 其他的默认
+
+linesP = cv2.HoughLinesP(edges, 1, np.pi/180, 80, minLineLength,maxLineGap)  
+
+
+
+result_P = img.copy()
+for i_P in linesP:
+    for x1, y1, x2, y2 in i_P:
+        cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
+
+cv2.imshow('Houghlines',result)
+cv2.imshow('Houghlinesp',result_P)
+cv2.waitKey(0)
+