├── .idea ├── .gitignore ├── Python.iml ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml └── modules.xml ├── README.md ├── image ├── high │ ├── 174941.jpg │ ├── 175112.jpg │ ├── 175145.jpg │ ├── 175231.jpg │ ├── 175319.jpg │ ├── 175404.jpg │ ├── 175453.jpg │ ├── 175522.jpg │ ├── 175919.jpg │ ├── 175941.jpg │ ├── 180117.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg └── low │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── main.py ├── result ├── bin │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── c │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── e │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── edges │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── end │ ├── 26817_175314.jpg │ ├── 26895_175224.jpg │ ├── 27056_180315.jpg │ ├── 28187_180353.jpg │ ├── 28245_175400.jpg │ ├── 28364_175515.jpg │ ├── 29411_175139.jpg │ ├── 29774_180214.jpg │ ├── 29803_175936.jpg │ ├── 32804_175446.jpg │ ├── 32945_175913.jpg │ ├── 33298_174937.jpg │ └── 35899_180020.jpg ├── input image │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── line_detect │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg └── re │ ├── 174937.jpg │ ├── 175139.jpg │ ├── 175224.jpg │ ├── 175314.jpg │ ├── 175400.jpg │ ├── 175446.jpg │ ├── 175515.jpg │ ├── 175913.jpg │ ├── 175936.jpg │ ├── 180020.jpg │ ├── 180214.jpg │ ├── 180315.jpg │ └── 180353.jpg ├── test.jpg ├── test.py ├── test1.jpg └── test2.jpg /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/Python.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineVisionEdgeDamageDefectDetection 2 | 机器视觉边缘破损缺陷检测 3 | 摘要：垫片是在机械生产过程中重要组成零件之一，其在生产过程中所产生边缘缺陷和破损会严重影响生产过程。目前国内的垫片边缘破损缺陷检测主要以人力为主，但因工作量大、检测速度较低、人眼易疲劳等问题，不仅限制底板零件的产出还会造成零件缺陷的误判，影响后续的使用。为此，本文提出一种基于机器视觉的垫片边缘破损缺陷自动检测方案。该方案对目标的小幅位移和旋转不敏感，同时通过设计一种基于模板匹配的缺陷检测方案兼顾了检测精度和处理效率。 4 | 5 | 关键词：缺陷检测，机器视觉，模板匹配 6 | 7 | ![image](https://user-images.githubusercontent.com/81954499/209473473-09486ec2-ba76-4bd8-96dc-bb3c2e4846ff.png) 8 | ![image](https://user-images.githubusercontent.com/81954499/209473491-cd0f139d-fc38-43a7-9d75-7526d44b50fc.png) 9 | -------------------------------------------------------------------------------- /image/high/174941.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lx497/MachineVisionEdgeDamageDefectDetection/392942bb9d576d21d2f2b02df4ce2f24245f37be/image/high/174941.jpg 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name = "" 13 | end_image_root = "result\\" 14 | 15 | def add_xy(add_lines): 16 | """ 17 | 增加点数，进行拟合，让长度长的直线，权重更大 18 | :param add_lines: 19 | :return: 20 | """ 21 | for line in add_lines: 22 | x1, y1, x2, y2 = line 23 | r = pow(pow(x1 - x2, 2) + pow(y1 - y2, 2), 0.5) 24 | if r < 50: 25 | continue 26 | xy_list = [] 27 | if x2 - x1 == 0: 28 | print("直线是竖直的") 29 | if y2 > y1: 30 | max_y = y2 31 | min_y = y1 32 | else: 33 | min_y = y2 34 | max_y = y1 35 | while min_y < max_y: 36 | min_y = min_y + 40 37 | xy_list.append([x1, min_y]) 38 | 39 | elif y2 - y1 == 0: 40 | print("直线是水平的") 41 | if x2 > x1: 42 | max_x = x2 43 | min_x = x1 44 | else: 45 | min_x = x2 46 | max_x = x1 47 | while min_x < max_x: 48 | min_x = min_x + 40 49 | xy_list.append([min_x, y1]) 50 | 51 | else: 52 | # 计算斜率 53 | k = -(y2 - y1) / (x2 - x1) 54 | b = y1 - k * x1 55 | # 求反正切，再将得到的弧度转换为度 56 | result_a = np.arctan(k) * 57.29577 57 | if -45 < result_a < 45: 58 | if x2 > x1: 59 | max_x = x2 60 | min_x = x1 61 | else: 62 | min_x = x2 63 | max_x = x1 64 | while min_x < max_x: 65 | min_x = min_x + 40 66 | xy_list.append([min_x, int(k * min_x + b)]) 67 | else: 68 | if y2 > y1: 69 | max_y = y2 70 | min_y = y1 71 | else: 72 | min_y = y2 73 | max_y = y1 74 | while min_y < max_y: 75 | min_y = min_y + 40 76 | xy_list.append([int((y1 - b) / k), min_y]) 77 | return xy_list 78 | 79 | 80 | def line_detect(image): 81 | img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 82 | line_vertical = [] 83 | line_horizontal = [] 84 | 85 | ret2, th2 = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 86 | 87 | kernel = np.ones((10, 10), np.uint8) 88 | # 3图像的开闭运算 89 | cv0pen = cv2.morphologyEx(th2, cv2.MORPH_OPEN, kernel) # 开运算 90 | 91 | edges = cv2.Canny(cv0pen, 50, 150, apertureSize=3) 92 | 93 | # 显示图片 94 | cv2.imwrite(end_image_root+"edges\\"+name, edges) 95 | # 检测白线 这里是设置检测直线的条件，可以去读一读HoughLinesP()函数，然后根据自己的要求设置检测条件 96 | lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 40, minLineLength=10, maxLineGap=10) 97 | i = 1 98 | # 对通过霍夫变换得到的数据进行遍历 99 | for line in lines: 100 | # newlines1 = lines[:, 0, :] 101 | # print("line[" + str(i - 1) + "]=", line) 102 | x1, y1, x2, y2 = line[0] # 两点确定一条直线，这里就是通过遍历得到的两个点的数据 （x1,y1）(x2,y2) 103 | cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2) # 在原图上画线 104 | # 转换为浮点数，计算斜率 105 | x1 = float(x1) 106 | x2 = float(x2) 107 | y1 = float(y1) 108 | y2 = float(y2) 109 | # print("x1=%s,x2=%s,y1=%s,y2=%s" % (x1, x2, y1, y2)) 110 | if x2 - x1 == 0: 111 | # print("直线是竖直的") 112 | 113 | line_vertical.append(line[0]) 114 | 115 | elif y2 - y1 == 0: 116 | # print("直线是水平的") 117 | 118 | line_horizontal.append(line[0]) 119 | 120 | else: 121 | # 计算斜率 122 | k = -(y2 - y1) / (x2 - x1) 123 | # 求反正切，再将得到的弧度转换为度 124 | result = np.arctan(k) * 57.29577 125 | if -45 < result < 45: 126 | line_horizontal.append(line[0]) 127 | else: 128 | line_vertical.append(line[0]) 129 | # print("直线倾斜角度为：" + str(result) + "度") 130 | i = i + 1 131 | # 显示最后的成果图 132 | cv2.imwrite(end_image_root+"line_detect\\"+name, image) 133 | return line_horizontal, line_vertical 134 | 135 | 136 | def cal_point(point_list): 137 | """ 138 | 139 | :param point_list: 140 | :return: 141 | """ 142 | xy = [] 143 | for point in point_list: 144 | xy.append([point[0], point[1]]) 145 | xy.append([point[2], point[3]]) 146 | return xy 147 | 148 | 149 | def cal_xy_point(xy_points): 150 | """ 151 | 152 | :param xy_points: 153 | :return: 154 | """ 155 | point_x = [] 156 | point_y = [] 157 | for point in xy_points: 158 | point_x.append(point[0]) 159 | point_y.append(point[1]) 160 | return point_x, point_y 161 | 162 | 163 | def linefit(x, y): 164 | """ 165 | 拟合曲线 166 | :param x: 167 | :param y: 168 | :return: 169 | """ 170 | N = float(len(x)) 171 | sx, sy, sxx, syy, sxy = 0, 0, 0, 0, 0 172 | for i in range(0, int(N)): 173 | sx += x[i] 174 | sy += y[i] 175 | sxx += x[i] * x[i] 176 | syy += y[i] * y[i] 177 | sxy += x[i] * y[i] 178 | a = (sy * sx / N - sxy) / (sx * sx / N - sxx) 179 | b = (sy - a * sx) / N 180 | r = abs(sy * sx / N - sxy) / math.sqrt((sxx - sx * sx / N) * (syy - sy * sy / N)) 181 | return a, b, r 182 | 183 | 184 | # 定义直线拟合函数 185 | def linear_regression(x, y): 186 | N = len(x) 187 | sumx = sum(x) 188 | sumy = sum(y) 189 | sumx2 = sum(x ** 2) 190 | sumxy = sum(x * y) 191 | 192 | A = np.mat([[N, sumx], [sumx, sumx2]]) 193 | b = np.array([sumy, sumxy]) 194 | 195 | return np.linalg.solve(A, b) 196 | 197 | 198 | def cal_a_and_b(points, flag): 199 | """ 200 | 计算最大最小值 201 | :param points: 202 | :param flag: falg为1 为垂直检测，0为水平检测 203 | :return: 204 | """ 205 | print(points) 206 | max_value = points[0][flag] 207 | min_value = points[0][flag] 208 | min_point = points[0] 209 | max_point = points[0] 210 | for point in points: 211 | print(point[flag]) 212 | if point[flag] > max_value: 213 | max_value = point[flag] 214 | max_point = point 215 | if point[flag] < min_value: 216 | min_value = point[flag] 217 | min_point = point 218 | x1, y1 = max_point 219 | x2, y2 = min_point 220 | k = -(y2 - y1) / (x2 - x1) 221 | c = y2 - k * x2 222 | return k, c 223 | 224 | 225 | def del_point(lines): 226 | temp_list = [] 227 | for line in lines: 228 | x1, y1, x2, y2 = line 229 | r = pow(pow(x1 - x2, 2) + pow(y1 - y2, 2), 0.5) 230 | if r < 100: 231 | continue 232 | else: 233 | temp_list.append(line) 234 | return temp_list 235 | 236 | 237 | def cal_k_ave(lines): 238 | """ 239 | 求斜率平均值 240 | :param lines: 241 | :return: 242 | """ 243 | new_lines = [] 244 | total_length = 0 # 全部总长 245 | bia_total_len = 0 # 斜线总长 246 | bia_lines = [] # 放斜线坐标 247 | bia_line = [] 248 | temp_lines = [] 249 | end_k = 0.0 250 | end_b = 0.0 251 | angle = 0.0 252 | p = 0.0 253 | b = 0.0 254 | p_v = 0.0 255 | p_bia = 0.0 256 | for line in lines: 257 | x1, y1, x2, y2 = line 258 | r = int(pow(pow(x1 - x2, 2) + pow(y1 - y2, 2), 0.5)) 259 | new_line = list(line) 260 | total_length = total_length + r 261 | new_line.append(r) 262 | new_lines.append(new_line) 263 | 264 | for new_line in new_lines: 265 | x1, y1, x2, y2, r = new_line 266 | p = r / total_length 267 | if x1 - x2 != 0: 268 | p_bia = p_bia + p # 概率加 269 | bia_total_len = bia_total_len + r 270 | k = (y2 - y1) / (x2 - x1) 271 | b = y1 - k * x1 272 | bia_line = [*new_line, k, b] 273 | 274 | bia_lines.append(bia_line) 275 | else: 276 | p_v = p_v + p # 概率加 277 | if p_v > 0.5: 278 | return [] 279 | else: 280 | rr = 0 281 | for bia_line in bia_lines: 282 | x1, y1, x2, y2, r, k, b = bia_line 283 | p = r / bia_total_len 284 | end_k = end_k + k * p 285 | end_b = end_b + b * p 286 | rr = rr + p 287 | if abs(np.arctan(k) * 57.29577 - np.arctan(end_k) * 57.29577) > 20: 288 | bia_lines.remove(bia_line) 289 | for temp in bia_lines: 290 | temp_lines.append(temp[0:4]) 291 | 292 | end_k, end_b = cal_k_ave(temp_lines) 293 | return [end_k, end_b] 294 | 295 | 296 | def calc_abc_from_line_2d(x0, y0, x1, y1): 297 | a = y0 - y1 298 | b = x1 - x0 299 | c = x0 * y1 - x1 * y0 300 | return a, b, c 301 | 302 | 303 | def get_line_cross_point(line1, line2): 304 | """ 305 | 计算交点 306 | :param line1: 307 | :param line2: 308 | :return: 309 | """ 310 | a0, b0, c0 = calc_abc_from_line_2d(*line1) 311 | a1, b1, c1 = calc_abc_from_line_2d(*line2) 312 | D = a0 * b1 - a1 * b0 313 | if D == 0: 314 | return None 315 | x = (b0 * c1 - b1 * c0) / D 316 | y = (a1 * c0 - a0 * c1) / D 317 | return x, y 318 | 319 | 320 | def cal_k_ave_h(lines): 321 | """ 322 | 求斜率平均值 323 | :param lines: 324 | :return: 325 | """ 326 | new_lines = [] 327 | total_length = 0 # 全部总长 328 | end_k = 0.0 329 | end_b = 0.0 330 | end_lines = [] 331 | temp_lines = [] 332 | 333 | for line in lines: 334 | x1, y1, x2, y2 = line 335 | r = int(pow(pow(x1 - x2, 2) + pow(y1 - y2, 2), 0.5)) 336 | new_line = list(line) 337 | total_length = total_length + r 338 | new_line.append(r) 339 | new_lines.append(new_line) 340 | for new_line in new_lines: 341 | x1, y1, x2, y2, r = new_line 342 | p = r / total_length 343 | 344 | k = (y2 - y1) / (x2 - x1) 345 | b = y1 - k * x1 346 | temp_line = [*new_line, k, b] 347 | end_lines.append(temp_line) 348 | 349 | end_k = end_k + k * p 350 | end_b = end_b + b * p 351 | for end_line in end_lines: 352 | x1, y1, x2, y2, r, k, b = end_line 353 | if abs(np.arctan(k) * 57.29577 - np.arctan(end_k) * 57.29577) > 10: 354 | end_lines.remove(end_line) 355 | for temp in end_lines: 356 | temp_lines.append(temp[0:4]) 357 | 358 | end_k, end_b = cal_k_ave_h(temp_lines) 359 | 360 | return [end_k, end_b] 361 | 362 | 363 | def main(path, file_name): 364 | # 画点参数 365 | point_size = 1 366 | point_color = (0, 0, 255) # BGR 367 | thickness = 2 368 | 369 | temp_x = [] 370 | temp_y = [] 371 | line_l = [] # 左 372 | line_r = [] # 右 373 | line_t = [] # top 374 | line_d = [] # down 375 | # 读入图片 376 | src = cv2.imread(path) 377 | image_temp = src.copy() 378 | h, w, _ = image_temp.shape 379 | 380 | # 显示原始图片 381 | cv2.imwrite(end_image_root+"input image\\" + file_name, src) 382 | 383 | # 图像二值化 384 | image_bin_temp = src.copy() 385 | img = cv2.cvtColor(image_bin_temp, cv2.COLOR_BGR2GRAY) 386 | 387 | ret2, image_bin = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 388 | # 调用函数 389 | horizontal, vertical = line_detect(src) 390 | 391 | # 遍历horizontal求y的均值进行判断是上端还是下端 392 | for temp in horizontal: 393 | temp_x.append(temp[1]) 394 | temp_x.append(temp[3]) 395 | average_h = np.mean(temp_x) 396 | for temp in horizontal: 397 | if temp[1] > average_h and temp[3] > average_h: 398 | line_d.append(temp) 399 | else: 400 | line_t.append(temp) 401 | 402 | for temp in vertical: 403 | temp_y.append(temp[0]) 404 | temp_y.append(temp[2]) 405 | average_v = np.mean(temp_y) 406 | for temp in vertical: 407 | if temp[0] > average_v and temp[2] > average_v: 408 | line_r.append(temp) 409 | else: 410 | line_l.append(temp) 411 | 412 | # 转化为xy型式，便于拟合 413 | point_t = cal_point(line_t) 414 | point_d = cal_point(line_d) 415 | point_l = cal_point(line_l) 416 | point_r = cal_point(line_r) 417 | 418 | # 转化形式，用于拟合 419 | point_t_x, point_t_y = cal_xy_point(point_t) 420 | point_d_x, point_d_y = cal_xy_point(point_d) 421 | point_l_x, point_l_y = cal_xy_point(point_l) 422 | point_r_x, point_r_y = cal_xy_point(point_r) 423 | # 424 | total_point = [] 425 | re_l = cal_k_ave(line_l) 426 | if len(re_l): 427 | al, bl = re_l 428 | point_l1 = (int(-bl / al) - 2, 0) # 用来画直线 429 | point_l2 = (int((h - bl) / al) - 2, h) 430 | cv2.line(image_temp, point_l1, point_l2, (0, 255, 0), thickness=2) 431 | else: 432 | 433 | point_l1 = (point_l_x[0] - 2, 0) # 用来画直线 434 | point_l2 = (point_l_x[0] - 2, h) 435 | 436 | cv2.line(image_temp, point_l1, point_l2, (0, 255, 0), thickness=2) 437 | 438 | re_r = cal_k_ave(line_r) 439 | 440 | if len(re_r): 441 | ar, br = re_r 442 | point_r1 = (int(-br / ar), 0) # 用来画直线 443 | point_r2 = (int((h - br) / ar), h) 444 | cv2.line(image_temp, point_r1, point_r2, (0, 255, 0), thickness=2) 445 | 446 | 447 | else: 448 | point_r1 = (point_r_x[0] + 2, 0) # 用来画直线 449 | point_r2 = (point_r_x[0] + 2, h) 450 | cv2.line(image_temp, point_r1, point_r2, (0, 255, 0), thickness=2) 451 | 452 | re_t = cal_k_ave_h(line_t) 453 | if len(re_t): 454 | tr_point = [] 455 | tl_point = [] 456 | at, bt = re_t 457 | point_t1 = (0, int(bt) - 2) # 用来画直线 458 | point_t2 = (w, int(w * at + bt) - 2) 459 | cv2.line(image_temp, point_t1, point_t2, (0, 255, 0), thickness=2) 460 | # 垂直时计算交点 461 | if point_r1[0] - point_r2[0] == 0: 462 | temp_xx = point_r1[0] 463 | temp_yy = int(round(point_r1[0] * at + bt, 0)) 464 | tr_point.append(temp_xx) 465 | tr_point.append(temp_yy) 466 | else: 467 | # 算出交点 468 | tr_point = get_line_cross_point([*list(point_t1), *list(point_t2)], [*list(point_r1), *list(point_r2)]) 469 | # 取整四舍五入 470 | point = (int(round(tr_point[0], 0)), int(round(tr_point[1], 0))) # 点的坐标。画点实际上就是画半径很小的实心圆。 471 | cv2.circle(image_temp, point, point_size, point_color, thickness) 472 | total_point.append(list(point)) 473 | 474 | # 垂直时计算交点 475 | if point_l1[0] - point_l2[0] == 0: 476 | temp_xx = point_l1[0] 477 | temp_yy = int(round(point_l1[0] * at + bt, 0)) 478 | tl_point.append(temp_xx) 479 | tl_point.append(temp_yy) 480 | else: 481 | tl_point = get_line_cross_point([*list(point_t1), *list(point_t2)], [*list(point_l1), *list(point_l2)]) 482 | point = (int(round(tl_point[0], 0)), int(round(tl_point[1], 0))) # 点的坐标。画点实际上就是画半径很小的实心圆。 483 | cv2.circle(image_temp, point, point_size, point_color, thickness) 484 | total_point.append(list(point)) 485 | 486 | 487 | else: 488 | print("t error") 489 | 490 | re_d = cal_k_ave_h(line_d) 491 | if len(re_d): 492 | dr_point = [] 493 | dl_point = [] 494 | ad, bd = re_d 495 | point_d1 = (0, int(bd) + 2) # 用来画直线 496 | point_d2 = (w, int(w * ad + bd) + 2) 497 | cv2.line(image_temp, point_d1, point_d2, (0, 255, 0), thickness=2) 498 | 499 | # 垂直时计算交点 500 | if point_r1[0] - point_r2[0] == 0: 501 | temp_xx = point_r1[0] 502 | temp_yy = int(round(point_r1[0] * ad + bd, 0)) 503 | dr_point.append(temp_xx) 504 | dr_point.append(temp_yy) 505 | else: 506 | # 算出交点 507 | dr_point = get_line_cross_point([*list(point_d1), *list(point_d2)], [*list(point_r1), *list(point_r2)]) 508 | # 取整四舍五入 509 | point = (int(round(dr_point[0], 0)), int(round(dr_point[1], 0))) # 点的坐标。画点实际上就是画半径很小的实心圆。 510 | cv2.circle(image_temp, point, point_size, point_color, thickness) 511 | total_point.append(list(point)) 512 | 513 | # 垂直时计算交点 514 | if point_l1[0] - point_l2[0] == 0: 515 | temp_xx = point_l1[0] 516 | temp_yy = int(round(point_l1[0] * ad + bd, 0)) 517 | dl_point.append(temp_xx) 518 | dl_point.append(temp_yy) 519 | else: 520 | dl_point = get_line_cross_point([*list(point_d1), *list(point_d2)], [*list(point_l1), *list(point_l2)]) 521 | point = (int(round(dl_point[0], 0)), int(round(dl_point[1], 0))) # 点的坐标。画点实际上就是画半径很小的实心圆。 522 | cv2.circle(image_temp, point, point_size, point_color, thickness) 523 | total_point.append(list(point)) 524 | 525 | else: 526 | print("d error") 527 | 528 | # print(total_point) 529 | pointSrc = np.float32(total_point) # 原始图像中 4点坐标 530 | pointDst = np.float32([[[0, 0], [500, 0], [0, 500], [500, 500]]]) # 变换图像中 4点坐标 531 | MP = cv2.getPerspectiveTransform(pointSrc, pointDst) # 计算投影变换矩阵 M 532 | imgP = cv2.warpPerspective(image_bin, MP, (500, 500)) # 用变换矩阵 M 进行投影变换 533 | 534 | # pointSrc = np.float32(total_point) # 原始图像中 4点坐标 535 | # pointDst = np.float32([[[0, 0], [500, 0], [0, 500], [500, 500]]]) # 变换图像中 4点坐标 536 | # MP = cv2.getPerspectiveTransform(pointDst, pointSrc) # 计算投影变换矩阵 M 537 | # re_imgP = cv2.warpPerspective(imgP, MP, (w, h)) # 用变换矩阵 M 进行投影变换 538 | # cv2.imwrite("re" + file_name, re_imgP) 539 | 540 | cv2.imwrite(end_image_root+"e\\" + file_name, imgP) 541 | cv2.imwrite(end_image_root+"c\\"+file_name, image_temp) 542 | return imgP, pointSrc, pointDst 543 | 544 | 545 | if __name__ == '__main__': 546 | rootDir = "D:\\SYS_File\\Desktop\\Python\\image\\low\\" 547 | filename = '175446.jpg' 548 | template = img = np.zeros((500, 500), dtype=np.uint8) 549 | main_image = cv2.imread(rootDir + filename) 550 | h, w, _ = main_image.shape 551 | # 获取模板 552 | t = len(2 * os.listdir(rootDir)) 553 | from tqdm import tqdm 554 | 555 | bar = tqdm(total=t) 556 | for filename in os.listdir(rootDir): 557 | name = filename 558 | pathname = os.path.join(rootDir, filename) 559 | img, pointSrc, pointDst = main(pathname, filename) 560 | img_row = img.shape[0] 561 | img_col = img.shape[1] 562 | 563 | # 利用遍历操作对二维灰度图作取反操作 564 | for i in range(img_row): 565 | for j in range(img_col): 566 | temp1 = img[i, j] 567 | temp2 = template[i, j] 568 | if temp1 or temp2: 569 | template[i, j] = 255 570 | else: 571 | template[i, j] = 0 572 | bar.update(1) 573 | # # 比较 574 | damaged_point = [] 575 | for filename in os.listdir(rootDir): 576 | name = filename 577 | end_image = cv2.imread(rootDir + filename) 578 | draw_image = end_image.copy() 579 | # 图像二值化 580 | image_bin_temp = end_image.copy() 581 | image_bin_temp = cv2.cvtColor(image_bin_temp, cv2.COLOR_BGR2GRAY) 582 | # 边缘缺失 583 | ret2, image_bin = cv2.threshold(image_bin_temp, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 584 | # 边缘破损 585 | 586 | ret, dam_img_bin = cv2.threshold(src=image_bin_temp, thresh=200, maxval=255, type=cv2.THRESH_BINARY) 587 | 588 | pathname = os.path.join(rootDir, filename) 589 | img, pointSrc, pointDst = main(pathname, filename) # 这里主要是反映射坐标求 590 | 591 | # 把模板反映射 592 | MP = cv2.getPerspectiveTransform(pointDst, pointSrc) # 计算投影变换矩阵 M 593 | end_template = cv2.warpPerspective(template, MP, (w, h)) # 用变换矩阵 M 进行投影变换 594 | cv2.imwrite(end_image_root+"re\\" + filename, end_template) 595 | 596 | img_row = image_bin.shape[0] 597 | img_col = image_bin.shape[1] 598 | 599 | cv2.imwrite(end_image_root+"bin\\" + filename, image_bin) 600 | # 利用遍历操作对二维灰度图作取反操作 601 | for i in range(img_row): 602 | for j in range(img_col): 603 | if end_template[i, j] == 255 and image_bin[i, j] == 0: 604 | damaged_point.append([i, j]) 605 | 606 | if dam_img_bin[i, j] == 255 and end_template[i, j] == 255: 607 | damaged_point.append([i, j]) 608 | 609 | for point in damaged_point: 610 | draw_image[point[0], point[1]] = 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np.ones((10, 10), np.uint8) 15 | # 3图像的开闭运算 16 | cv0pen = cv2.morphologyEx(th2, cv2.MORPH_OPEN, kernel) # 开运算 17 | cv2.imwrite("test1.jpg", th2) 18 | 19 | edges = cv2.Canny(cv0pen, 50, 150, apertureSize=3) 20 | cv2.imwrite("test2.jpg", edges) -------------------------------------------------------------------------------- /test1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lx497/MachineVisionEdgeDamageDefectDetection/392942bb9d576d21d2f2b02df4ce2f24245f37be/test1.jpg -------------------------------------------------------------------------------- /test2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lx497/MachineVisionEdgeDamageDefectDetection/392942bb9d576d21d2f2b02df4ce2f24245f37be/test2.jpg --------------------------------------------------------------------------------