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opencv 截图leiding_cv2.sobel(img, cv2.cv_16sc1, 1, 0)

作者：小丑西瓜9 | 2024-02-27 01:05:36

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cv2.sobel(img, cv2.cv_16sc1, 1, 0)

opencv 这个效果差一点

  1. import cv2
  2. import numpy as np
  3. import os
  4. import time
  5.  
  6. GAUSSIAN_BLUR_KERNEL_SIZE=(5,5)
  7. GAUSSIAN_BLUR_SIGMA_X=0
  8. CANNY_THRESHOLD1=60
  9. CANNY_THRESHOLD2=250
  10. LEEXING=4
  11.  
  12. def get_gaussian_blur_image(image):
  13.     return cv2.GaussianBlur(image,GAUSSIAN_BLUR_KERNEL_SIZE,GAUSSIAN_BLUR_SIGMA_X)
  14.  
  15. def get_canny_image(image):
  16.     return cv2.Canny(image,CANNY_THRESHOLD1,CANNY_THRESHOLD2)
  17.  
  18. def get_contours(image):
  19.     contours,_=cv2.findContours(image,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
  20.     return contours
  21.  
  22. def lp(imgsss):
  23.     img1=cv2.imread(imgsss,0)
  24.     x1=cv2.Sobel(img1,cv2.CV_16S,1,0,ksize=-1)
  25.     y1=cv2.Sobel(img1,cv2.CV_16S,0,1,ksize=-1)
  26.     Scale_absX1=cv2.convertScaleAbs(x1)
  27.     Scale_absY1=cv2.convertScaleAbs(y1)
  28.     result1=cv2.addWeighted(Scale_absX1,0.5,Scale_absY1,0.5,0)
  29.     return result1
  30.  
  31. def ceshi1(imgsss):
  32.     image_raw = cv2.imread(imgsss)
  33.     h, w, _ = image_raw.shape
  34.     mianji_MIN1 = 15000
  35.     mianji_MAX1 = 40000
  36.     zhouchang_min = 80
  37.     zhouchang_max = 1800
  38.  
  39.     offset_min = 0.5 * image_raw.shape[1]
  40.     offset_max = 0.95 * image_raw.shape[1]
  41.  
  42.  
  43.     result1 = lp(imgsss)
  44.     # 高斯去噪
  45.     result2 = get_gaussian_blur_image(result1)
  46.     # 均值
  47.     result3 = cv2.blur(result2, (3, 3))
  48.     # 提取輪廓
  49.     image_canny = get_canny_image(result3)
  50.  
  51.     # 膨脹
  52.     kernel = np.ones((3, 3), np.uint8)
  53.     dige_dilate1 = cv2.dilate(image_canny, kernel, iterations=1)
  54.     # 中值
  55.     median = cv2.medianBlur(dige_dilate1, 5)
  56.  
  57.     contours = get_contours(median)
  58.  
  59.     image = cv2.imread(imgsss)
  60.     contour = image.copy()
  61.     cv2.drawContours(contour, contours, -1, (0, 255, 0), 2)
  62.     count = 0  # 个数
  63.     margin = 5  # 裁剪边距
  64.     draw_rect = image.copy()
  65.     for i, contour in enumerate(contours):
  66.         area = cv2.contourArea(contour)  # 计算包围形状的面积
  67.         zhouchang = cv2.arcLength(contour, True)
  68.         x2, y2, w2, h2 = cv2.boundingRect(contour)
  69.         if area < mianji_MIN1:  # 过滤面积小于15的形状
  70.             continue
  71.         if area > mianji_MAX1:  # 过滤面积小于15的形状
  72.             continue
  73.         if zhouchang < zhouchang_min:
  74.             continue
  75.         if zhouchang > zhouchang_max:
  76.             continue
  77.         if x2 < offset_min:
  78.             continue
  79.         if x2 > offset_max:
  80.             continue
  81.  
  82.         print('面積：', area, '周長：', zhouchang)
  83.         count += 1
  84.         rect = cv2.minAreaRect(contour)  # 检测轮廓最小外接矩形，得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
  85.         box = np.int0(cv2.boxPoints(rect))  # 获取最小外接矩形的4个顶点坐标
  86.         print(box)
  87.         txt11=txt1(q=box,width=w,height=h)
  88.         txt22=str(LEEXING)+' '+str(txt11[0])+' '+str(txt11[1])+' '+str(txt11[2])+' '+str(txt11[3])
  89.         print(txt22)
  90.  
  91.         cv2.drawContours(draw_rect, [box], 0, (0, 0, 255), 2)  # 绘制轮廓最小外接矩形
  92.  
  93.         h, w = image.shape[:2]  # 原图像的高和宽
  94.         rect_w, rect_h = int(rect[1][0]) + 1, int(rect[1][1]) + 1  # 最小外接矩形的宽和高
  95.         if rect_w <= rect_h:
  96.             x, y = int(box[1][0]), int(box[1][1])  # 旋转中心
  97.             M2 = cv2.getRotationMatrix2D((x, y), rect[2], 1)
  98.             rotated_image = cv2.warpAffine(image, M2, (w * 2, h * 2))
  99.             y1, y2 = y - margin if y - margin > 0 else 0, y + rect_h + margin + 1
  100.             x1, x2 = x - margin if x - margin > 0 else 0, x + rect_w + margin + 1
  101.             rotated_canvas = rotated_image[y1: y2, x1: x2]
  102.         else:
  103.             x, y = int(box[2][0]), int(box[2][1])  # 旋转中心
  104.             M2 = cv2.getRotationMatrix2D((x, y), rect[2] + 90, 1)
  105.             rotated_image = cv2.warpAffine(image, M2, (w * 2, h * 2))
  106.             y1, y2 = y - margin if y - margin > 0 else 0, y + rect_w + margin + 1
  107.             x1, x2 = x - margin if x - margin > 0 else 0, x + rect_h + margin + 1
  108.             rotated_canvas = rotated_image[y1: y2, x1: x2]
  109.         print("rice #{}".format(count))
  110.         # cv2.imshow("rotated_canvas", rotated_canvas)
  111.         imgs = imgsss.split('\\')
  112.         cv2.imwrite("./imgs/{}.jpg".format(imgs[-1]+str(count)), rotated_canvas)
  113.         with open("./imgs/{}.txt".format(imgs[-1]+str(count)), 'w') as fp:
  114.             fp.write(txt22)
  115.         cv2.waitKey(0)
  116.     cv2.imwrite("rect.jpg", draw_rect)
  117.  
  118. def wenjian(path1):
  119.     df_list=[]
  120.     list212=os.listdir(path1)
  121.     for i in list212:
  122.         file_path=os.path.join(path1,i)
  123.         df_list.append(file_path)
  124.     return df_list
  125.  
  126. def txt1(q,width,height):
  127.     x1=q[1][0]
  128.     x2=q[3][0]
  129.     y1=q[1][1]
  130.     y2=q[3][1]
  131.     xmin_xishu, xmax_xishu =x1 / width, x2 / width
  132.     ymin_xishu, ymax_xishu= y1 / height ,y2 / height
  133.     width2=xmax_xishu-xmin_xishu
  134.     height2=ymax_xishu-ymin_xishu
  135.     x_centr_shu=xmin_xishu+width2/2
  136.     y_centr_shu=ymin_xishu+height2/2
  137.     return round(x_centr_shu, 6),round(y_centr_shu, 6),round(width2, 6),round(height2, 6)
  138.  
  139. if __name__ == '__main__':
  140.     a=time.time()
  141.     path1 = r'E:\0908\1WVE'  # 路径
  142.     list1=wenjian(path1)
  143.     for i in list1:
  144.         ceshi1(i)
  145.     b=time.time()
  146.     c=b-a
  147.     print(c)

 这个效果可以

  1. import cv2
  2. import numpy as np
  3. import os
  4. import time
  5.  
  6. GAUSSIAN_BLUR_KERNEL_SIZE=(5,5)
  7. GAUSSIAN_BLUR_SIGMA_X=0
  8. CANNY_THRESHOLD1=120
  9. CANNY_THRESHOLD2=250
  10. LEEXING=12     #要识别的类
  11.  
  12. def get_gaussian_blur_image(image):    #高斯去噪
  13.     return cv2.GaussianBlur(image,GAUSSIAN_BLUR_KERNEL_SIZE,GAUSSIAN_BLUR_SIGMA_X)
  14.  
  15. def get_canny_image(image):         #找轮廓
  16.     return cv2.Canny(image,CANNY_THRESHOLD1,CANNY_THRESHOLD2)
  17.  
  18. def get_contours(image):        #导出轮廓参数
  19.     contours,_=cv2.findContours(image,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
  20.     return contours
  21.  
  22. def lp(imgsss):      #算轮廓
  23.     img1=cv2.imread(imgsss,0)
  24.     x1=cv2.Sobel(img1,cv2.CV_16S,1,0,ksize=-1)
  25.     y1=cv2.Sobel(img1,cv2.CV_16S,0,1,ksize=-1)
  26.     Scale_absX1=cv2.convertScaleAbs(x1)
  27.     Scale_absY1=cv2.convertScaleAbs(y1)
  28.     result1=cv2.addWeighted(Scale_absX1,0.5,Scale_absY1,0.5,0)
  29.     return result1
  30.  
  31. def ceshi1(imgsss):
  32.     image_raw = cv2.imread(imgsss)
  33.     h, w, _ = image_raw.shape
  34.  
  35.     mianji_MIN1 = 700000          #最小面积
  36.     mianji_MAX1 = 1000000          #3最大面积
  37.     zhouchang_min = 3000          #最小周长
  38.     zhouchang_max = 5000            最大周长
  39.     offset_min = 0.4 * image_raw.shape[1]      #最小偏移量
  40.     offset_max = 0.99 * image_raw.shape[1]      #最大偏移量
  41.  
  42.  
  43.  
  44.     img = cv2.imread(imgsss)
  45.     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)       #一个算子也可以改改成lp函数里的算子
  46.     blurred = cv2.GaussianBlur(gray, (3, 3), 0)
  47.     xgrad = cv2.Sobel(blurred, cv2.CV_16SC1, 1, 0)
  48.     ygrad = cv2.Sobel(blurred, cv2.CV_16SC1, 0, 1)
  49.     edge_output = cv2.Canny(xgrad, ygrad, 50, 150)
  50.     rect_Kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
  51.     image_tophat = cv2.morphologyEx(edge_output, cv2.MORPH_CLOSE, rect_Kernel)
  52.  
  53.     contours = get_contours(image_tophat)
  54.  
  55.     image = cv2.imread(imgsss)
  56.     contour = image.copy()
  57.     cv2.drawContours(contour, contours, -1, (0, 255, 0), 2)
  58.     count = 0  # 个数
  59.     margin = 5  # 裁剪边距
  60.     draw_rect = image.copy()
  61.     for i, contour in enumerate(contours):
  62.         area = cv2.contourArea(contour)  # 计算包围形状的面积
  63.         zhouchang = cv2.arcLength(contour, True)
  64.         x2, y2, w2, h2 = cv2.boundingRect(contour)
  65.         if area < mianji_MIN1:  # 过滤面积小于15的形状
  66.             continue
  67.         if area > mianji_MAX1:  # 过滤面积小于15的形状
  68.             continue
  69.         if zhouchang < zhouchang_min:
  70.             continue
  71.         if zhouchang > zhouchang_max:
  72.             continue
  73.         if x2 < offset_min:
  74.             continue
  75.         if x2 > offset_max:
  76.             continue
  77.  
  78.         print('面積：', area, '周長：', zhouchang)
  79.         count += 1
  80.         rect = cv2.minAreaRect(contour)  # 检测轮廓最小外接矩形，得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
  81.         box = np.int0(cv2.boxPoints(rect))  # 获取最小外接矩形的4个顶点坐标
  82.         # print(box)
  83.         txt11=txt1(q=box,width=w,height=h)
  84.         txt22=str(LEEXING)+' '+str(txt11[0])+' '+str(txt11[1])+' '+str(txt11[2])+' '+str(txt11[3])
  85.         print(txt22)
  86.  
  87.         cv2.drawContours(draw_rect, [box], 0, (0, 0, 255), 2)  # 绘制轮廓最小外接矩形
  88.  
  89.         h, w = image.shape[:2]  # 原图像的高和宽
  90.         rect_w, rect_h = int(rect[1][0]) + 1, int(rect[1][1]) + 1  # 最小外接矩形的宽和高
  91.         if rect_w <= rect_h:
  92.             x, y = int(box[1][0]), int(box[1][1])  # 旋转中心
  93.             M2 = cv2.getRotationMatrix2D((x, y), rect[2], 1)
  94.             rotated_image = cv2.warpAffine(image, M2, (w * 2, h * 2))
  95.             y1, y2 = y - margin if y - margin > 0 else 0, y + rect_h + margin + 1
  96.             x1, x2 = x - margin if x - margin > 0 else 0, x + rect_w + margin + 1
  97.             rotated_canvas = rotated_image[y1: y2, x1: x2]
  98.         else:
  99.             x, y = int(box[2][0]), int(box[2][1])  # 旋转中心
  100.             M2 = cv2.getRotationMatrix2D((x, y), rect[2] + 90, 1)
  101.             rotated_image = cv2.warpAffine(image, M2, (w * 2, h * 2))
  102.             y1, y2 = y - margin if y - margin > 0 else 0, y + rect_w + margin + 1
  103.             x1, x2 = x - margin if x - margin > 0 else 0, x + rect_h + margin + 1
  104.             rotated_canvas = rotated_image[y1: y2, x1: x2]
  105.         print("rice #{}".format(count))
  106.         # cv2.imshow("rotated_canvas", rotated_canvas)
  107.         imgs = imgsss.split('\\')
  108.         cv2.imwrite("./imgs/{}.jpg".format(imgs[-1]+str(count)), rotated_canvas)
  109.         with open("./imgs/{}.txt".format(imgs[-1]+str(count)), 'w') as fp:
  110.             fp.write(txt22)
  111.         cv2.waitKey(0)
  112.         cv2.imwrite("./imgs/{}.png".format(imgs[-1]+str(count)), draw_rect)
  113.     # cv2.imwrite("rect.jpg", draw_rect)
  114.  
  115. def wenjian(path1):
  116.     df_list=[]             #读取文件夹里的照片
  117.     list212=os.listdir(path1)
  118.     for i in list212:
  119.         file_path=os.path.join(path1,i)
  120.         df_list.append(file_path)
  121.     return df_list
  122.  
  123. def txt1(q,width,height):  #计算出需要的参数
  124.     x1=q[1][0]
  125.     x2=q[3][0]
  126.     y1=q[1][1]
  127.     y2=q[3][1]
  128.     xmin_xishu, xmax_xishu =x1 / width, x2 / width
  129.     ymin_xishu, ymax_xishu= y1 / height ,y2 / height
  130.     width2=xmax_xishu-xmin_xishu
  131.     height2=ymax_xishu-ymin_xishu
  132.     x_centr_shu=xmin_xishu+width2/2
  133.     y_centr_shu=ymin_xishu+height2/2
  134.     return round(x_centr_shu, 6),round(y_centr_shu, 6),round(width2, 6),round(height2, 6)
  135.  
  136. if __name__ == '__main__':
  137.     a=time.time()
  138.     path1 = r'D:\AOI\18HP'  # 路径
  139.     list1=wenjian(path1)
  140.     for i in list1:
  141.         print(i)
  142.         ceshi1(i)
  143.     b=time.time()
  144.     c=b-a
  145.     print(c)

单独跑一张图片的

 

  1. import cv2 as cv
  2. import numpy as np
  3. import time
  4. from matplotlib import pyplot as plt
  5.  
  6.  
  7. # kernel = np.ones((3,3),np.uint8)  #腐蝕
  8. # dige_erosion = cv.erode(image_canny,kernel,iterations = 1)
  9. #
  10. # kernel = np.ones((3,3),np.uint8)  #膨脹
  11. # dige_dilate = cv.dilate(dige_erosion,kernel,iterations = 2)
  12.  
  13. a=time.time()
  14.  
  15. imgsss='1.jpg'
  16.  
  17. CANNY_THRESHOLD1=60
  18. CANNY_THRESHOLD2=150
  19. GAUSSIAN_BLUR_KERNEL_SIZE=(5,5)
  20. GAUSSIAN_BLUR_SIGMA_X=0
  21.  
  22.  
  23. image_raw=cv.imread(imgsss)
  24.  
  25. mianji_MIN1=700000
  26. mianji_MAX1=1000000
  27. zhouchang_min=3000
  28. zhouchang_max=5000
  29. offset_min=0.4*image_raw.shape[1]
  30. offset_max=0.99*image_raw.shape[1]
  31.  
  32. #拉普算子
  33. def lp(imgsss):
  34.     img1=cv.imread(imgsss,0)
  35.     x1=cv.Sobel(img1,cv.CV_16S,1,0,ksize=-1)
  36.     y1=cv.Sobel(img1,cv.CV_16S,0,1,ksize=-1)
  37.     Scale_absX1=cv.convertScaleAbs(x1)
  38.     Scale_absY1=cv.convertScaleAbs(y1)
  39.     result1=cv.addWeighted(Scale_absX1,0.5,Scale_absY1,0.5,0)
  40.     return result1
  41.  
  42. def zhi():
  43.     image = cv.imread(imgsss, cv.IMREAD_GRAYSCALE)
  44.     thresh1 = cv.adaptiveThreshold(image, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 11, 2)
  45.     thresh2 = cv.adaptiveThreshold(image, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 11, 2)
  46.     thresh3 = cv.adaptiveThreshold(image, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11, 2)
  47.     thresh4 = cv.adaptiveThreshold(image, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 11, 2)
  48.     return thresh1,thresh2,thresh3,thresh4
  49.  
  50.  
  51. def erzhi():
  52.     # 读取第一张图像
  53.     img = cv.imread(imgsss)
  54.     # 获取图像尺寸
  55.     h, w = img.shape[0:2]
  56.     # 自定义空白单通道图像，用于存放灰度图
  57.     gray = np.zeros((h, w), dtype=img.dtype)
  58.     # 对原图像进行遍历，然后分别对B\G\R按比例灰度化
  59.     for i in range(h):
  60.         for j in range(w):
  61.             gray[i, j] = max(img[i, j, 0], img[i, j, 1], img[i, j, 2])  # 求3通道中最大的值
  62.     gray = cv.cvtColor(gray, cv.COLOR_BGR2RGB)  # BGR转换为RGB显示格式，方便通过matplotlib进行图像显示
  63.  
  64.     ref_BINARY = cv.threshold(gray, 180, 255, cv.THRESH_BINARY_INV)[1]  # 转换成二值图像
  65.     return ref_BINARY
  66.  
  67. '''高斯去噪'''
  68. def get_gaussian_blur_image(image):   #高斯去噪
  69.     return cv.GaussianBlur(image,GAUSSIAN_BLUR_KERNEL_SIZE,GAUSSIAN_BLUR_SIGMA_X)
  70.  
  71. def get_canny_image(image):
  72.     return cv.Canny(image,CANNY_THRESHOLD1,CANNY_THRESHOLD2)
  73.  
  74. def get_contours(image):
  75.     contours,_=cv.findContours(image,cv.RETR_CCOMP,cv.CHAIN_APPROX_SIMPLE)
  76.     return contours
  77.  
  78.  
  79. img = cv.imread(imgsss)
  80. gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
  81. blurred = cv.GaussianBlur(gray, (3, 3), 0)
  82. xgrad = cv.Sobel(blurred, cv.CV_16SC1, 1, 0)
  83. ygrad = cv.Sobel(blurred, cv.CV_16SC1, 0, 1)
  84. edge_output = cv.Canny(xgrad, ygrad, 50, 150)
  85. rect_Kernel = cv.getStructuringElement(cv.MORPH_RECT, (9, 3))
  86. image_tophat = cv.morphologyEx(edge_output, cv.MORPH_CLOSE, rect_Kernel)
  87.  
  88. # #高斯去噪
  89. # result2=get_gaussian_blur_image(result1)
  90. # #均值
  91. # result3 = cv.blur(result2,(3,3))
  92. #提取輪廓
  93. # image_canny=get_canny_image(s1)
  94.  
  95. #膨脹
  96. # kernel = np.ones((3,3),np.uint8)
  97. # dige_dilate1 = cv.dilate(image_canny,kernel,iterations = 1)
  98. #中值
  99. # median = cv.medianBlur(image_canny,5)
  100.  
  101. contours=get_contours(image_tophat)
  102.  
  103.  
  104. image = cv.imread(imgsss)
  105. contour = image.copy()
  106. cv.drawContours(contour, contours, -1, (0, 255, 0), 2)
  107. count = 0  # 个数
  108. margin = 5  # 裁剪边距
  109. draw_rect = image.copy()
  110. for i, contour in enumerate(contours):
  111.     area = cv.contourArea(contour)  # 计算包围形状的面积
  112.     zhouchang = cv.arcLength(contour, True)
  113.     x2,y2,w2,h2=cv.boundingRect(contour)
  114.     if area < mianji_MIN1:  # 过滤面积小于15的形状
  115.         continue
  116.     if area >mianji_MAX1:  # 过滤面积小于15的形状
  117.         continue
  118.     if zhouchang<zhouchang_min:
  119.         continue
  120.     if zhouchang>zhouchang_max:
  121.         continue
  122.     if x2 < offset_min:
  123.         continue
  124.     if x2 > offset_max:
  125.         continue
  126.  
  127.     print('面積：',area ,'周長：',zhouchang)
  128.     count += 1
  129.     rect = cv.minAreaRect(contour)  # 检测轮廓最小外接矩形，得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
  130.     box = np.int0(cv.boxPoints(rect))  # 获取最小外接矩形的4个顶点坐标
  131.     # print(box)
  132.     cv.drawContours(draw_rect, [box], 0, (0, 0,255), 2)  # 绘制轮廓最小外接矩形
  133.  
  134.     h, w = image.shape[:2]  # 原图像的高和宽
  135.     rect_w, rect_h = int(rect[1][0]) + 1, int(rect[1][1]) + 1  # 最小外接矩形的宽和高
  136.     if rect_w <= rect_h:
  137.         x, y = int(box[1][0]), int(box[1][1])  # 旋转中心
  138.         M2 = cv.getRotationMatrix2D((x, y), rect[2], 1)
  139.         rotated_image = cv.warpAffine(image, M2, (w * 2, h * 2))
  140.         y1, y2 = y - margin if y - margin > 0 else 0, y + rect_h + margin + 1
  141.         x1, x2 = x - margin if x - margin > 0 else 0, x + rect_w + margin + 1
  142.         rotated_canvas = rotated_image[y1: y2, x1: x2]
  143.     else:
  144.         x, y = int(box[2][0]), int(box[2][1])  # 旋转中心
  145.         M2 = cv.getRotationMatrix2D((x, y), rect[2] + 90, 1)
  146.         rotated_image = cv.warpAffine(image, M2, (w * 2, h * 2))
  147.         y1, y2 = y - margin if y - margin > 0 else 0, y + rect_w + margin + 1
  148.         x1, x2 = x - margin if x - margin > 0 else 0, x + rect_h + margin + 1
  149.         rotated_canvas = rotated_image[y1: y2, x1: x2]
  150.     print("rice #{}".format(count))
  151.     # cv2.imshow("rotated_canvas", rotated_canvas)
  152.     cv.imwrite("./imgs/{}.jpg".format(count), rotated_canvas)
  153.     cv.waitKey(0)
  154. cv.imwrite("rect.jpg", draw_rect)
  155.  
  156. b=time.time()
  157. c=b-a
  158. print(c)
  159. plt.figure()
  160. plt.imshow(image_tophat)
  161. plt.title("space")
  162. plt.show()
  163.  
  164.

Opencv图像处理（全）_胖墩会武术的博客-CSDN博客

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