def SegMentLWelder(mask_k, mask_box, img_show, contours_info, box, k_class): imgL = cv2.copyTo(mask_k, mask_box) contours_mask_k, _ = cv2.findContours( imgL, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) (x, y), radius = cv2.minEnclosingCircle(contours_mask_k[0]) center = [int(x), int(y)] area, trg1 = cv2.minEnclosingTriangle(contours_mask_k[0]) triange_P1 = trg1[0][0] triange_P2 = trg1[1][0] triange_P3 = trg1[2][0] areaL = len(np.where(imgL == 255)[0]) triangle_cnt1 = np.array([triange_P1, center, triange_P2]) mask_triange = np.zeros_like(imgL) mask_triange = cv2.drawContours( mask_triange, [triangle_cnt1.astype(int)], 0, 255, -1) img_triange1 = cv2.copyTo(imgL, mask_triange) triangle_cnt2 = np.array([triange_P2, center, triange_P3]) mask_triange = np.zeros_like(imgL) mask_triange = cv2.drawContours( mask_triange, [triangle_cnt2.astype(int)], 0, 255, -1) img_triange2 = cv2.copyTo(imgL, mask_triange) area1 = len(np.where(img_triange1 == 255)[0]) if area1 < 0.25*areaL or area1 > 0.75*areaL: triangle_cnt1 = np.array([triange_P1, center, triange_P3]) mask_triange = np.zeros_like(imgL) mask_triange = cv2.drawContours( mask_triange, [triangle_cnt1.astype(int)], 0, 255, -1) img_triange1 = cv2.copyTo(imgL, mask_triange) else: area2 = len(np.where(img_triange2 == 255)[0]) if area2 < 0.25*areaL or area2 > 0.75*areaL: triangle_cnt2 = np.array([triange_P1, center, triange_P3]) mask_triange = np.zeros_like(imgL) mask_triange = cv2.drawContours( mask_triange, [triangle_cnt2.astype(int)], 0, 255, -1) img_triange2 = cv2.copyTo(imgL, mask_triange) FindContourSinge(k_class, img_triange1, contours_info, img_show, times=1) FindContourSinge(k_class, img_triange2, contours_info, img_show, times=1)

def FindContourSinge(k_class, mask_k, contours_info, img_show, times=0): contours, _ = cv2.findContours( mask_k, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) for i, c in enumerate(contours): area = cv2.contourArea(c) print(f"area {i}:{area}") if area < 200: # 去除小轮廓 continue

这段代码定义了一个名为FindContourSinge的函数，用于在给定的二值图像mask_k中查找轮廓信息，并将这些轮廓信息存储到contours_info列表中。同时，函数还会在img_show上绘制这些轮廓，并通过times参数控制绘制轮廓的...

优化这段代码import cv2 import imutils import numpy as np img = cv2.imread('D:\pycharm\PycharmProjects\pythonProject\p1\p1.jpg', cv2.IMREAD_COLOR) img = cv2.resize(img, (600, 400)) cv2.imshow('Origin image', img) img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img_gray = cv2.bilateralFilter(img_gray, 13, 15, 15) img_edged = cv2.Canny(img_gray, 30, 200) cv2.imshow('edged image', img_edged) img_contours = cv2.findContours(img_edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) img_contours = imutils.grab_contours(img_contours) img_contours = sorted(img_contours, key=cv2.contourArea, reverse=True)[:10] screenCnt = None for c in img_contours: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.018 * peri, True) if len(approx) == 4: screenCnt = approx break if screenCnt is None: detected = 0 print("No contour detected") else: detected = 1 if detected == 1: cv2.drawContours(img, [screenCnt], -1, (0, 0, 255), 3) mask = np.zeros(img_gray.shape, np.uint8) new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1, ) cv2.imshow('mask_image', new_image) new_image = cv2.bitwise_and(img, img, mask=mask) (x, y) = np.where(mask == 255) (topx, topy) = (np.min(x), np.min(y)) (bottomx, bottomy) = (np.max(x), np.max(y)) cropped = img_gray[topx:bottomx + 1, topy:bottomy + 1] cropped = cv2.resize(cropped, (400, 200)) cv2.imshow('Cropped', cropped) cv2.waitKey(0) cv2.destroyAllWindows()

5. 使用with语句来管理资源，例如cv2.VideoCapture和cv2.imshow，以确保资源的正确释放。 6. 使用argparse模块来解析命令行参数，以方便指定不同的参数和选项。 7. 使用更具描述性的变量名，以增加代码的...

# 根据每个轮廓的类型, 找到对应的焊缝轨迹 paths = [] self.welding_pts_3d = [] for k, info in enumerate(contours_info): c_type = info["type"] shape, path, pts_3d, end_point_2d = self.FindTypePath(pm, info) if path is None: vision_log.error(f"no path in {k} contour") img_show = cv2.drawContours( img_show, [info["box"].astype(int)], 0, (0, 0, 0), 5) continue for end_2d in end_point_2d: cv2.circle(img_show, tuple(end_2d.astype( np.int32).tolist()), 5, (0, 0, 0), 5) img_show = cv2.drawContours( img_show, [info["box"].astype(int)], 0, (255, 0, 0), 2) path_info = {} path_info["type"] = info["type"].value path_info['shape'] = shape path_info["path"] = path path_info["pts_3d"] = pts_3d.tolist() paths.append(path_info)

然后，遍历contours_info数组中的每个轮廓信息，获取其类型c_type，并调用FindTypePath()函数，传入pm数组和该轮廓的信息info，返回该轮廓的形状shape、轨迹path、三维点集pts_3d和终点坐标end_point_2d。...

FindContourSinge(k_class, img_triange1, contours_info, img_show, times=1) FindContourSinge(k_class, img_triange2, contours_info, img_show, times=1)

这段代码调用了一个名为...2.调用FindContourSinge函数，传入k_class、img_triange2、contours_info、img_show和times作为参数，得到img_triange2中的轮廓信息contours_info，并在img_show上绘制这些轮廓。

def chose_licence_plate(contours, Min_Area=2000): temp_contours = [] for contour in contours: if cv2.contourArea(contour) > Min_Area: temp_contours.append(contour) car_plate = [] for temp_contour in temp_contours: rect_tupple = cv2.minAreaRect(temp_contour) rect_width, rect_height = rect_tupple[1] if rect_width < rect_height: rect_width, rect_height = rect_height, rect_width aspect_ratio = rect_width / rect_height # 车牌正常情况下宽高比在2 - 5.5之间 if aspect_ratio > 2 and aspect_ratio < 5.5: car_plate.append(temp_contour) rect_vertices = cv2.boxPoints(rect_tupple) rect_vertices = np.int0(rect_vertices) return car_plate。能详细解释这段代码吗

其中，参数contours是输入的轮廓列表，Min_Area是最小面积阈值，默认为2000。函数首先对输入的轮廓进行筛选，只保留面积大于Min_Area的轮廓，然后对这些轮廓进行进一步处理，计算其最小外接矩形的宽高比，如果宽比高...

解释以下代码：def recognize_digit(test_image, template_images): contours, _ = cv2.findContours(test_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if contours: # 假设只有一个数字，取最大的轮廓 max_contour = max(contours, key=cv2.contourArea) x, y, w, h = cv2.boundingRect(max_contour) digit_roi = test_image[y:y + h, x:x + w] # 调整数字ROI大小为模板大小 digit_roi = cv2.resize(digit_roi, (32, 32)) # 模板匹配，找到最佳匹配的数字 best_match_index = -1 best_match_score = float('inf') for i, template in enumerate(template_images): result = cv2.matchTemplate(digit_roi, template, cv2.TM_SQDIFF_NORMED) min_val, _, _, _ = cv2.minMaxLoc(result) if min_val < best_match_score: best_match_score = min_val best_match_index = i return best_match_index else: return None

contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) digit_contours = [] for contour in contours: (x, y, w, h) = cv2.boundingRect(contour) # 如果矩形框满足...

import numpy as np from matplotlib import pyplot as plt import cv2 as cv def contours_area(cnt): # 计算 countour 的面积 (x, y, w, h) = cv.boundingRect(cnt) return w * h # 载入原图 img = cv.imread('cat.png') # 图像二值化 img_bin = cv.inRange(img, lowerb=(9, 16, 84), upperb=(255, 251, 255)) kernel = np.ones((5, 5), np.uint8) img_bin = cv.erode(img_bin, kernel, iterations=1) img_bin = cv.dilate(img_bin, kernel, iterations=2) contours, hierarchy = cv.findContours(img_bin,cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) img_contours=cv.drawContours(img_bin, contours, -1, (0, 0, 255), 2) #抠图-带罩层的二值化与操作 #img跟它本身进行或/与操作(其实他们的结果是一样的) 在罩层区域(MASK)内进行。罩层区域为0, 黑色。 #二值化操作就是如果两个img的该点的像素点都不为零则保留原来的取值，否则就是黑色。 mask = img.copy() mask[:] = 0 cv.drawContours(mask, contours, -1, (255,255,255), -1) img_cutout = cv.bitwise_or(img,mask) background=np.zeros_like(img) background[:,:,:]=(150,198,12) new_background = cv.bitwise_or(background, background, mask=cv.bitwise_not(mask)) new_img=cv.add(new_background,img_cutout) cv.imshow('binary',new_img) cv.waitKey(0) cv.destroyAllWindows

这是一个Python函数的...导入了numpy，matplotlib和cv2三个模块；该函数接受一个参数cnt，表示轮廓（contours）；函数主要功能为计算轮廓的面积（area），并返回该值；具体实现需要调用OpenCV库中的相关函数。

red_contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) blue_contours, _ = cv2.findContours(blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) green_contours, _ = cv2.findContours(green_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) yellow_contours, _ = cv2.findContours(yellow_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

red_contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) blue_contours, _ = cv2.findContours(blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) green_contours, _ = ...

import cv2 import numpy as np def main(): # 1.导入图片 img_src = cv2.imread("cc.jpg") # 2.灰度化,二值化 img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY) ret, img_bin = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY) # 3.连通域分析 img_bin, contours, hierarchy = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 4.获取最小外接矩形并打印 min_rect = cv2.minAreaRect(contours[0]) print("返回值min_rect:\n", min_rect) rect_points = cv2.boxPoints(min_rect) print("返回值rect_points:\n", rect_points) rect_points = np.int0(rect_points) # 5.绘制最小外接矩形 img_result = img_src.copy() cv2.drawContours(img_result, [rect_points], 0, (255, 255, 255), 2) # 6.显示结果图片 cv2.imshow("img_src", img_src) cv2.imshow("img_result", img_result) cv2.waitKey() cv2.destroyAllWindows() if name == 'main': main()怎么把图像摆正

img_result_rotated = cv2.warpAffine(img_result, M, img_src.shape[1::-1], flags=cv2.INTER_CUBIC) 这将生成一个旋转后的图像img_result_rotated。您可以将其保存为图像文件并使用cv2.imshow()函数显示。

def detect_shapes(frame): # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # 红色范围 lower_red = np.array([0, 100, 100]) upper_red = np.array([10, 255, 255]) red_mask1 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([160, 100, 100]) upper_red = np.array([179, 255, 255]) red_mask2 = cv2.inRange(hsv, lower_red, upper_red) red_mask = red_mask1 + red_mask2 # 蓝色范围 lower_blue = np.array([90, 100, 100]) upper_blue = np.array([130, 255, 255]) blue_mask = cv2.inRange(hsv, lower_blue, upper_blue) # 查找轮廓 contours, _ = cv2.findContours(red_mask + blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: # 计算轮廓的近似形状 epsilon = 0.02 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) # 获取轮廓的外接矩形 x, y, w, h = cv2.boundingRect(approx) # 根据轮廓的顶点数和颜色进行分类 if len(approx) == 3: if np.any(red_mask[y:y+h, x:x+w]): shape_label = "Red Triangle" else: shape_label = "Blue Triangle" elif len(approx) == 4: if np.any(red_mask[y:y+h, x:x+w]): shape_label = "Red Square" else: shape_label = "Blue Square" elif len(approx) > 4: if np.any(red_mask[y:y+h, x:x+w]): shape_label = "Red Circle" else: shape_label = "Blue Circle" else: shape_label = "Unknown" # 在图像上绘制边界框和标签https://cdn-static-devbit.csdn.net/ai100/chat/imgs/icon-send-active.png cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) cv2.putText(frame, shape_label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0),

它首先将图像转换为HSV颜色空间，然后定义了红色和蓝色的颜色范围，并通过调用cv2.inRange()函数创建了红色和蓝色的掩膜。接下来，它通过调用cv2.findContours()函数查找图像中的轮廓。对于每个轮廓，它计算...

detections = detections[detections.confidence > threshold] masks = detections.mask conf = detections.confidence class_ids = detections.class_id results = [] if masks.shape[0] > 0: for label, score, seg in zip(class_ids, conf, masks): # 解析分割掩码 mask = seg.argmax(axis=0) # 取最大值的索引，将二维掩码还原为一维 mask = mask.astype(np.uint8) # 将掩码转换为无符号整数类型 _, mask_binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY) # 将掩码转换为二值图像 # 获取分割多边形 contours, _ = cv2.findContours(mask_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contour = contours[0] if len(contours) > 0 else [] # 将分割多边形的点坐标转换为列表的点（points）形式 points = contour.squeeze().tolist() if len(contour) > 0 else []增加判断对象有检测到才进行

contours, _ = cv2.findContours(mask_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contour = contours[0] if len(contours) > 0 else [] # 将分割多边形的点坐标转换为列表的点（points）形式 ...

这段代码是在做什么？ def binary_img_segment(binary_img, original_img=None): # binary_img = skeletonize(binary_img) # plot.imshow( binary_img,cmap = 'gray', interpolation = 'bicubic') # plot.show() # 寻找每一个字符的轮廓，使用cv2.RETR_EXTERNAL模式，表示只需要每一个字符最外面的轮廓 img, contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # cv2.RETR_TREE # cv2.drawContours(img_original, contours, -1, (0, 255, 0), 2) if len(contours) > LARGEST_NUMBER_OF_SYMBOLS: raise ValueError('symtem cannot interpret this image!') symbol_segment_location = [] # 将每一个联通体，作为一个字符 symbol_segment_list = [] index = 1 for contour in contours: location = cv2.boundingRect(contour) x, y, w, h = location if (w * h < 100): continue symbol_segment_location.append(location) # 只提取轮廓内的字符 extracted_img = extract_img(location, img, contour) symbol_segment_list.append(extracted_img) if len(original_img): cv2.rectangle(original_img, (x, y), (x + w, y + h), (0, 0, 255), 3) symbols = [] for i in range(len(symbol_segment_location)): symbols.append({'location': symbol_segment_location[i], 'src_img': symbol_segment_list[i]}) # 对字符按字符横坐标排序 symbols.sort(key=lambda x: x['location'][0]) return symbols

它的输入是一个二值图像(binary_img)和一个可选的原始图像(original_img)。函数的目标是找到图像中每个字符的轮廓，并将它们作为单独的字符进行分割。首先，使用cv2.findContours函数找到二值图像中的轮廓。这里...

import cv2 import numpy as np import openpyxl from openpyxl.utils import get_column_letter def process_image(image, wb, sheet, frame_count, scale_percent=0.35): try: # 应用高斯滤波去噪 blurred = cv2.GaussianBlur(image, (5, 5), 0) # 优化缩放比例计算 height, width = image.shape[:2] new_width = int(width * scale_percent) new_height = int(height * scale_percent) resized = cv2.resize(blurred, (new_width, new_height), cv2.INTER_AREA) # 改进的HSV阈值范围（扩展红色检测范围） hsv = cv2.cvtColor(resized, cv2.COLOR_BGR2HSV) lower_red1 = np.array([0, 50, 50]) # 调整饱和度/亮度下限 upper_red1 = np.array([10, 255, 255]) lower_red2 = np.array([160, 50, 50]) # 修正第二个红色区域 upper_red2 = np.array([180, 255, 255]) # 创建组合掩膜 mask1 = cv2.inRange(hsv, lower_red1, upper_red1) mask2 = cv2.inRange(hsv, lower_red2, upper_red2) mask = cv2.bitwise_or(mask1, mask2) # 增强形态学处理 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2) mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2) # 轮廓检测优化 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if contours: # 多条件筛选轮廓 valid_contours = [] for c in contours: area = cv2.contourArea(c) if area < 100: # 过滤小面积噪点 continue # 圆形度筛选 perimeter = cv2.arcLength(c, True) if perimeter == 0: continue circularity = 4 * np.pi * area / (perimeter ** 2) if circularity < 0.7: # 排除非圆形目标 continue valid_contours.append(c) if not valid_contours: return # 选择最大轮廓 max_contour = max(valid_contours, key=cv2.contourArea)

mask = cv.bitwise_or(mask1, mask2) # 合并两个红色区域 #### 二、形态学操作优化使用$3\times3$结构核进行噪声消除： python kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3,3)) mask = cv....

# find contours of each welding line # 根据mask的每个类别, 用opencv找轮廓方法进行分类 n_class = self.config["n_class"] img_show, contours_info = FindContour(img, imgROI, n_class)

这段代码的功能是在一个图像中找到每个焊接线的轮廓，并将轮廓信息保存到 contours_info 变量中。具体来说，首先从配置文件中读取 n_class，即图像中需要分割的类别数。然后调用 FindContour 函数，传入原始图像 ...

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if boxW/boxh > 0.5 and times == 0: SegMentLWelder(mask_k, mask_box, img_show, contours_info, box, k_class)

for k_class in range(1, n_class): # 遍历每个类找到对应的轮廓, 相当于分类 mask_k = np.zeros((h_mask, m_mask)).astype(np.uint8) mask_k[np.where(mask == k_class)] = 255 FindContourSinge(k_class, mask_k, contours_info, img_show) return img_show, contours_info

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if boxW/boxh > 0.5 and times == 0: SegMentLWelder(mask_k, mask_box, img_show, contours_info, box, k_class)

for k_class in range(1, n_class): # 遍历每个类找到对应的轮廓, 相当于分类 mask_k = np.zeros((h_mask, m_mask)).astype(np.uint8) mask_k[np.where(mask == k_class)] = 255 FindContourSinge(k_class, mask_k, contours_info, img_show) return img_show, contours_info

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def FindContourSinge(k_class, mask_k, contours_info, img_show, times=0): contours, _ = cv2.findContours( mask_k, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) for i, c in enumerate(contours): area = cv2.contourArea(c) print(f"area {i}:{area}") if area < 200: # 去除小轮廓 continue

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# find contours of each welding line # 根据mask的每个类别, 用opencv找轮廓方法进行分类 n_class = self.config["n_class"] img_show, contours_info = FindContour(img, imgROI, n_class)

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