add HoughlinesP folder and demo

HoughlinesP/HoughLinesP-distance.py

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+import cv2,os
+import numpy as np
+
+dir_path = "pic/"
+
+files = os.listdir(dir_path)
+
+print(files)
+
+for file in files:
+    png_name = "pic/" + file
+    if os.path.isfile(png_name):
+        # Read image
+        image = cv2.imread(png_name)
+
+        cv2.namedWindow('Non-Adjacent Lines', cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)
+
+        # Convert image to grayscale
+        # gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+
+        # Use canny edge detection
+        edges = cv2.Canny(image,10,50,apertureSize=3)
+
+        # 使用HoughLinesP检测线段
+        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=250, maxLineGap=1)
+
+        # 如果lines不为空，则处理检测到的线段
+        if lines is not None:
+            # 将检测到的线段转换为整数坐标
+            lines = lines.reshape(-1, 4)
+            
+            # 设置相邻线段的距离阈值
+            distance_threshold = 20  # 根据实际情况调整这个值
+            
+            # 用于存储非相邻线段的列表
+            non_adjacent_lines = []
+            
+            # 遍历检测到的线段
+            for i in range(len(lines)):
+                x1, y1, x2, y2 = lines[i]
+                is_adjacent = False
+                
+                # 检查当前线段是否与已存储的非相邻线段相邻
+                for line in non_adjacent_lines:
+                    lx1, ly1, lx2, ly2 = line
+                    
+                    # 计算两条线段之间的最小距离（这里使用简单的欧几里得距离作为示例）
+                    # 注意：这种方法可能不够精确，因为它没有考虑线段的方向和长度
+                    # 在实际应用中，你可能需要使用更复杂的几何方法来计算线段之间的距离
+                    distance = min(
+                        np.linalg.norm([x1 - lx1, y1 - ly1]),
+                        np.linalg.norm([x1 - lx2, y1 - ly2]),
+                        np.linalg.norm([x2 - lx1, y2 - ly1]),
+                        np.linalg.norm([x2 - lx2, y2 - ly2])
+                    )
+                    
+                    # 如果距离小于阈值，则标记为相邻
+                    if distance < distance_threshold:
+                        is_adjacent = True
+                        break
+                
+                # 如果当前线段不是相邻的，则添加到非相邻线段列表中
+                if not is_adjacent:
+                    non_adjacent_lines.append((x1, y1, x2, y2))
+            
+            # 在图像上绘制非相邻线段
+            for line in non_adjacent_lines:
+                x1, y1, x2, y2 = line
+                print(x1,x2,y1,y2)
+                cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+            # 显示结果图像
+            pic_name='pic/result/houghlinesP-distance'+file
+            cv2.imwrite(pic_name,image)
+            #cv2.imshow('Non-Adjacent Lines', image)
+            #cv2.waitKey(0)
+            #cv2.destroyAllWindows()

HoughlinesP/HoughLinesP.py

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+import cv2
+import numpy as np
+
+cv2.namedWindow('img', cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)
+
+# Read image
+image = cv2.imread('pic/1.png')
+
+# Convert image to grayscale
+gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+
+# Use canny edge detection
+edges = cv2.Canny(gray,100,150,apertureSize=3)
+
+# Apply HoughLinesP method to 
+# to directly obtain line end points
+lines_list =[]
+lines = cv2.HoughLinesP(
+            edges, # Input edge image
+            1, # Distance resolution in pixels
+            np.pi/180, # Angle resolution in radians
+            threshold=100, # Min number of votes for valid line
+            minLineLength=250, # Min allowed length of line
+            maxLineGap=1 # Max allowed gap between line for joining them
+            )
+
+for line in lines:
+    for x1, y1, x2, y2 in line:
+      #if x1 == x2 or y1 == y2:
+        print(x1,x2,y1,y2)
+        cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+'''
+# Iterate over points
+for points in lines:
+      # Extracted points nested in the list
+    x1,y1,x2,y2=points[0]
+    # Draw the lines joing the points
+    # On the original image
+    cv2.line(image,(x1,y1),(x2,y2),(0,255,0),2)
+    # Maintain a simples lookup list for points
+    lines_list.append([(x1,y1),(x2,y2)])
+'''
+
+# Save the result image
+#cv2.imwrite('detectedLines.png',image)
+#cv2.imshow("image", gray) ##显示灰度图
+#cv2.imshow("image", edges) ##显示canny图
+cv2.imshow('img', image)
+cv2.waitKey(0)

HoughlinesP/README.md

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+# line-detection-project
+## HoughlinesP
+###  HoughlinesP为测试程序，实际实用程序为HoughlinesP-distance.py