rgb = imread('D:\7.png'); hsv = rgb2hsv(rgb); yellow = hsv(:,:,1) < 0.17 & hsv(:,:,2) > 0.4 & hsv(:,:,3) > 0.3; se = strel('disk', 5); yellow = imopen(yellow, se); bw = bwareaopen(yellow, 50); edge = edge(bw); [centers, radii] = imfindcircles(edge, [20 60], 'ObjectPolarity', 'bright', 'Sensitivity', 0.9); figure; imshow(rgb); viscircles(centers, radii,'EdgeColor','b'); hold on; for i = 1:size(centers, 1) fprintf('The centroid of flower %d is (%f, %f)\n', i, centers(i, 1), centers(i, 2)); plot(centers(i, 1), centers(i, 2), 'r+', 'MarkerSize', 10, 'LineWidth', 2); end hold off;将结果在图中显示出来

% 读取原始图像 RGB =imread('qingapple.jpg'); %将RGB空间图像转换为HSI空间图像 HSI = rgb2hsv(RGB); %显示彩色图像 subplot(1,2,1); imshow(RGB); title('原始图像'); %将绿色转换为蓝色 R = RGB(:,:,1); G = RGB(:,:,2); B = RGB(:,:,3); %根据绿色的颜色范围设置掩码 mask_green = G>50; %将绿色转换为蓝色 B(mask_green) = 255; RGB_new = cat(3,R,G,B); subplot(1,2,2); imshow(RGB_new); title('转换图像');分析一下这段matlab代码以及结果

具体地，首先通过 imread 函数读取原始图像，然后调用 rgb2hsv 函数将 RGB 图像转换为 HSI 空间的图像。接下来，将 RGB 图像分离为三个分量 R、G 和 B，然后根据绿色的颜色范围设置一个掩码 mask_green，以便将...

逐行解释下列代码的意思BGR_img = cv2.imread('fruit.jpg',1) def bgr_to_hsv(BGR_img): rows, cols, _ = BGR_img.shape HSV_img = np.zeros_like(BGR_img) for i in range(rows): for j in range(cols): b, g, r = BGR_img[i, j] b, g, r = b / 255.0, g / 255.0, r / 255.0 cmax = max(b, g, r) cmin = min(b, g, r) delta = cmax - cmin if delta == 0: h = 0 elif cmax == r: h = 30 * (((g - b) / delta) % 6) elif cmax == g: h = 30 * (((b - r) / delta) + 2) else: h = 30 * (((r - g) / delta) + 4) if cmax == 0: s = 0 else: s = delta / cmax v = cmax HSV_img[i, j] = [h, s * 255.0, v * 255.0] return HSV_img HSV_img = bgr_to_hsv(BGR_img) low = np.array([26,43,46]) high = np.array([34,255,255]) mask = np.zeros_like(HSV_img[:,:,0]) b = HSV_img[:,:,0] g = HSV_img[:,:,1] r = HSV_img[:,:,2] mask[(b >= low[0]) & (b <= high[0]) & (g >= low[1]) & (g <= high[1]) & (r >= low[2]) & (r <= high[2])] = 255 height,width = mask.shape mask = np.reshape(mask,(height,width,1)) / 255 mask_BGR = mask * BGR_img cv2.imwrite("mask_fruit.jpg", mask_BGR) plt.show()

1. BGR_img = cv2.imread('fruit.jpg',1)：读取名为'fruit.jpg'的图像，参数1表示以BGR格式读取，返回一个BGR格式的图像。 2. def bgr_to_hsv(BGR_img): 定义一个函数bgr_to_hsv，将输入的BGR格式的图像转换为HSV...

解释一下这些代码 import cv2 img = cv2.imread('image.png') hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lower_red = np.array([30,150,50]) upper_red = np.array([255,255,180]) mask = cv2.inRange(hsv, lower_red, upper_red) res = cv2.bitwise_and(img,img, mask= mask) cv2.imshow('img',img) cv2.imshow('mask',mask) cv2.imshow('res',res) cv2.waitKey(0) cv2.destroyAllWindows()

它先从图像中读取数据，然后将其转换为HSV颜色空间，定义红色的范围，使用inRange函数检测出红色部分，然后用bitwise_and函数显示红色部分，最后使用waitKey和destroyAllWindows函数来停止显示图像。

目标检测为什么要对数据集进行HSV色调图像处理，def he_hsv(img_demo): img_hsv = cv2.cvtColor(img_demo, cv2.COLOR_RGB2HSV) # Histogram equalisation on the V-channel img_hsv[:, :, 2] = cv2.equalizeHist(img_hsv[:, :, 2]) image_hsv = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2RGB) return image_hsv def clahe_hsv(img): hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, v = hsv_img[:,:,0], hsv_img[:,:,1], hsv_img[:,:,2] clahe = cv2.createCLAHE(clipLimit = 100.0, tileGridSize = (10,10)) v = clahe.apply(v) hsv_img = np.dstack((h,s,v)) rgb = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB) return rgb index = 40 image = cv2.imread(reef_df.iloc[index]['img_path']) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) fig, ax = plt.subplots(1,3,figsize=(20,20)) display_transformed_image(image, index, ax[0]) display_transformed_image(he_hsv(image), index, ax[1]) display_transformed_image(clahe_hsv(image), index, ax[2]) plt.show()这段代码是什么意思，HSV对于目标检测的优点是什么

HSV色彩空间是一种比RGB色彩空间更直观和自然的颜色表示方式，包含色调(Hue)、饱和度(Saturation)和明度(Value)，HSV色彩空间可以更好地模拟人类视觉系统的颜色感知方式。在目标检测中，HSV色彩空间可以用于调整...

这段代码有什么问题image = imread(uigetfile('C:\Users\Liang\Desktop\苹果\原始图像\.jpg','选择图片')); imshow(image); %img1=uigetfile('','选择文件'); % 将图像从 RGB 转换到 HSV 颜色空间 hsvImage = rgb2hsv(image); % 取出H,S,V各个分量 hue = hsvImage(:,:,1); saturation = hsvImage(:,:,2); value = hsvImage(:,:,3); % 使用颜色阈值来识别苹果的颜色 hueThresholdLow = 0.04; hueThresholdHigh = 0.1; saturationThresholdLow = 0.4; saturationThresholdHigh = 1; valueThresholdLow = 0.4; valueThresholdHigh = 1; % 根据阈值进行分割 binaryImage = (hue >= hueThresholdLow & hue <= hueThresholdHigh) & ... (saturation >= saturationThresholdLow & saturation <= saturationThresholdHigh) & ... (value >= valueThresholdLow & value <= valueThresholdHigh); imshow(binaryImage); % 使用形态学运算对二值图像进行处理 se = strel('disk', 7); morphedImage = imclose(binaryImage, se); morphedImage = imfill(morphedImage, 'holes'); imshow(morphedImage); % 对处理后的图像进行测量 measurements = regionprops(morphedImage, 'BoundingBox', 'Centroid'); %对处理后的图像进行测量 imshow(image); hold on; for k = 1:length(measurements) boundary = measurements(k).BoundingBox; centroid = measurements(k).Centroid; rectangle('Position', [boundary(1),boundary(2),boundary(3),boundary(4)], 'EdgeColor', 'r', 'LineWidth', 2) plot(centroid(1),centroid(2), 'b*') end hold off;

1. imread 函数读取文件时路径应该用双反斜杠或单斜杠，而不是单反斜杠。因此 uigetfile 函数应该改为 uigetfile('C:\\Users\\Liang\\Desktop\\苹果\\原始图像\\*.jpg','选择图片') 或 uigetfile('C:/Users/...

clear all; clc; source_img=imread('C:\Users\LENOVO\Desktop\yes.jpg');%读取图片 [m,n,p]=size(source_img);%计算图片的行数列数层数 %==========从RGB转换到HSV======================= hsv_img=rgb2hsv(source_img); h=hsv_img(:,:,1); s=hsv_img(:,:,2); v=hsv_img(:,:,3); figure; subplot(221);imshow(source_img); subplot(222);imshow(h); subplot(223);imshow(s); subplot(224);imshow(v); %============V分量小波包分解======================================== [cc,ss]=wavedec2(v,1,'haar'); cA=appcoef2(cc,ss,'haar',1); %cc:小波分解的小波系数矩阵；ss：小波分解对应的尺度矩阵；分解的层数为1 cH=detcoef2('h',cc,ss,1); %h:提取水平高频；v：垂直高频；d：对角高频 cV=detcoef2('v',cc,ss,1); cD=detcoef2('d',cc,ss,1); cA1=mapminmax(cA,0,1);%归一化处理 figure; subplot(221);imshow(cA1,[]);title('(a) 近似分量cA'); subplot(222);imshow(cH,[]);title('(b) 细节分量cH'); subplot(223);imshow(cV,[]);title('(c) 细节分量cV'); subplot(224);imshow(cD,[]);title('(d) 细节分量cD'); %=============近似分量cA双边滤波================================== w = 3; % bilateral filter half-width sigma = [3 0.2]; % bilateral filter standard deviations cA2=bfilter2(cA1,w,sigma); %双边滤波 hsize=15; sigma1=15; sigma2=85; sigma3=265; H1=fspecial('gaussian',hsize,sigma1); H2=fspecial('gaussian',hsize,sigma2); H3=fspecial('gaussian',hsize,sigma3); img1=conv2(v,H1,'same'); img2=conv2(v,H2,'same'); img3=conv2(v,H3,'same'); imggaus=1/3img1+1/3img2+1/3img3; alpha5=0.1; k5=alpha5sum(s(:))/(mn); vnew5=v(1+k5)./(max(v,imggaus)+k5); X1=v(:)'; X2=vnew5(:)'; X=[X1 X2]; C=cov(X1,X2); [V,D]=eig(C); diagD=diag(D); if diagD(1)>diagD(2) V1=V(:,1); else V1=V(:,2); end w1=V1(1)/(V1(1)+V1(2)); w2=V1(2)/(V1(1)+V1(2)); recon_set=w1v+w2vnew5; %==============HSV转换RGB========================================= hsv(:,:,1)=h; hsv(:,:,2)=s; hsv(:,:,3)=recon_set; rgb_img=hsv2rgb(hsv); figure; subplot(121);imshow(source_img);%显示原图 subplot(122);imshow(rgb_img);%显示增强后的图根据代码写结果说明及分析

8. 最后，将H、S、recon_set三个通道重新组合为HSV图像，并将其转换回RGB颜色空间，得到增强后的图像rgb_img。根据这段代码，您实现了一个基于HSV颜色空间和小波包分解的低照度图像增强方法，并结合了双边滤波和主...

% 读取图片 img = imread('image.jpg'); % 将RGB图像转换为HSV图像 hsv = rgb2hsv(img); % 获取图像的尺寸 [rows,cols,~] = size(hsv); % 初始化颜色计数器 red = 0; yellow = 0; green = 0; blue = 0; % 遍历图像的每个像素 for i = 1:rows for j = 1:cols % 获取像素的HSV值 h = hsv(i,j,1); s = hsv(i,j,2); v = hsv(i,j,3); % 判断像素的颜色 if (h >= 0 && h < 0.05) || (h >= 0.95 && h <= 1) if s > 0.4 && v > 0.4 red = red + 1; end elseif h >= 0.13 && h <= 0.18 if s > 0.4 && v > 0.4 yellow = yellow + 1; end elseif h >= 0.25 && h <= 0.4 if s > 0.4 && v > 0.4 green = green + 1; end elseif h >= 0.55 && h <= 0.75 if s > 0.4 && v > 0.4 blue = blue + 1; end end end end % 输出颜色计数器的值 disp(['Red: ', num2str(red)]); disp(['Yellow: ', num2str(yellow)]); disp(['Green: ', num2str(green)]); disp(['Blue: ', num2str(blue)]);

% 将RGB图像转换为HSV图像 hsv = rgb2hsv(img); % 获取图像的尺寸 [rows,cols,~] = size(hsv); % 初始化颜色计数器 colors = [0, 0, 0, 0]; % red, yellow, green, blue % 遍历图像的每个像素 for i = 1:rows for j...

[filename, pathname] = uigetfile({'.jpg';'.png'}, '选择图片');%黑白处理 if isequal(filename,0) disp('用户取消选择'); else Path=strcat(pathname,filename);%strcat横向拼接字符串得到所选文件的绝对路径 img=imread(Path);%通过绝对路径选择文件 hsv_img = rgb2hsv(img); sat_channel = hsv_img(:, :, 2); factor = 0; sat_channel = sat_channel * factor; hsv_img(:, :, 2) = sat_channel; output_img = hsv2rgb(hsv_img); app.Image.ImageSource=output_img; end注释这段代码

接着，将RGB色彩空间转换为HSV色彩空间，提取出饱和度通道，并将其乘以一个因子（即黑白处理的程度）。然后，将处理后的饱和度通道放回到HSV图像中，再将图像转换回RGB色彩空间。最后，将处理后的图像显示在GUI中，...

import cv2 import numpy as np # 读取图像 img = cv2.imread('image.jpg') # 将图像从RGB颜色空间转换为HSV颜色空间 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 定义红色的HSV范围 lower_red = np.array([, 50, 50]) upper_red = np.array([10, 255, 255]) mask1 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([170, 50, 50]) upper_red = np.array([180, 255, 255]) mask2 = cv2.inRange(hsv, lower_red, upper_red) # 将两个掩膜相加 mask = mask1 + mask2 # 对原始图像和掩膜进行位运算 res = cv2.bitwise_and(img, img, mask=mask) # 显示结果 cv2.imshow('image', img) cv2.imshow('mask', mask) cv2.imshow('res', res) cv2.waitKey() cv2.destroyAllWindows()改写为c++代码

// 将图像从RGB颜色空间转换为HSV颜色空间 cv::Mat hsv; cv::cvtColor(img, hsv, cv::COLOR_BGR2HSV); // 定义红色的HSV范围 cv::Scalar lower_red1(0, 50, 50); cv::Scalar upper_red1(10, 255, 255); cv::...

import cv2 import numpy as np import os # 提取图像的HOG特征 def get_hog_features(image): hog = cv2.HOGDescriptor() hog_features = hog.compute(image) return hog_features # 加载训练数据集 train_data = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128"] train_labels = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128\labels.txt"] num_samples = 681 for i in range(num_samples): img = cv2.imread(str(i).zfill(3)+'.jpg') hog_features = get_hog_features(image) hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) color_hist = cv2.calcHist([hsv_image], [0, 1], None, [180, 256], [0, 180, 0, 256]) color_features = cv2.normalize(color_hist, color_hist).flatten() train_data.append(hog_features) train_labels.append(labels[i]) # 训练SVM模型 svm = cv2.ml.SVM_create() svm.setType(cv2.ml.SVM_C_SVC) svm.setKernel(cv2.ml.SVM_LINEAR) svm.train(np.array(train_data), cv2.ml.ROW_SAMPLE, np.array(train_labels)) # 对测试图像进行分类 test_image = cv2.imread('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\maskslic2_roi.png', 0) test_features = get_hog_features(test_image) result = svm.predict(test_features.reshape(1,-1)) # 显示分割结果 result_image = np.zeros(test_image.shape, np.uint8) for i in range(test_image.shape[0]): for j in range(test_image.shape[1]): if result[i,j] == 1: result_image[i,j] = 255 cv2.imshow('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\Result.png', result_image) cv2.waitKey(0) cv2.destroyAllWindows()

test_image = cv2.imread('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\maskslic2_roi.png', 0) test_features = get_hog_features(test_image) hsv_test_image = cv2....

import cv2 import numpy as np # 读取相机拍摄的图像 image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange(hsv, lower_red, upper_red)完善上述代码，显示结果

image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) ...

import cv2 import numpy as np # 读取相机拍摄的图像 image = cv2.imread('1.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange(hsv, lower_red, upper_red) #cv2.imshow('Mask', mask) # 进行形态学操作，去除噪声 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # 检测红色瓶盖的轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 对轮廓进行筛选和排序，选择两个最大的轮廓 contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2]请完善上述代码。显示结果

image = cv2.imread('1.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255...

import cv2 import numpy as np # 加载图像 img = cv2.imread('color_blind_road_2.png') # 转换为灰度图像 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 直线检测 edges = cv2.Canny(gray, 50, 150, apertureSize=3) lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10) for line in lines: x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2) # 阈值分割 ret, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) thresh = cv2.medianBlur(thresh, 5) # 彩色连续性空间分割 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) mask = cv2.inRange(h, 0, 20) | cv2.inRange(h, 160, 180) mask = cv2.bitwise_and(thresh, mask) # 纹理分割 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) texture = cv2.Canny(gray, 100, 200, apertureSize=3) texture = cv2.dilate(texture, None, iterations=3) texture = cv2.erode(texture, None, iterations=3) texture = cv2.bitwise_and(thresh, texture) # 显示结果 cv2.imshow('img', img) cv2.imshow('thresh', thresh) cv2.imshow('color', mask) cv2.imshow('texture', texture) cv2.waitKey(0) cv2.destroyAllWindows()改进代码

img = cv2.imread(path) return img except Exception as e: print(e) return None def gray_transform(img): """灰度转换""" gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return gray def edge_...

以下代码发生TypeError: Expected Ptrcv::UMat for argument 'mat'，代码如下： def on_pushButton_5_clicked(self): # 读取左相机图像 left_image_path = '1_left.JPG' # 替换为实际图像的路径 left_image = cv2.imread(left_image_path) # 转换为HSV颜色空间 hsv_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2HSV) # 定义红色的HSV颜色范围 lower_red = np.array([0, 100, 100]) upper_red = np.array([10, 255, 255]) # 对图像进行红色阈值处理 red_mask = cv2.inRange(hsv_image, lower_red, upper_red) # 执行形态学操作，去除噪声 kernel = np.ones((5, 5), np.uint8) red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel) # 查找红色轮廓 contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 保留最大的两个轮廓 contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2] # 遍历轮廓并绘制圆心和坐标 for contour in contours: # 计算轮廓的最小外接圆 (x, y), radius = cv2.minEnclosingCircle(contour) center = (int(x), int(y)) radius = int(radius) # 绘制圆心 cv2.circle(left_image, center, 3, (0, 255, 0), -1) # 绘制圆形轮廓 cv2.circle(left_image, center, radius, (0, 0, 255), 2) # 绘制坐标 text = f'({int(x)}, {int(y)})' cv2.putText(left_image, text, (int(x) + 10, int(y) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) cv2.imshow('Result', cv2.circle) cv2.waitKey() showImg = cv2.cvtColor('image', cv2.COLOR_BGR2RGB) qImgae = QImage(showImg, showImg.shape[1], showImg.shape[0], showImg.shape[1]*3, QImage.Format_RGB888) self.label.setPixmap(QPixmap(qImage).scaled(self.label.width(), self.label.height(), Qt.KeepAspectRatio))

left_image = cv2.imread(left_image_path) # 转换为HSV颜色空间 hsv_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2HSV) # 定义红色的HSV颜色范围 lower_red = np.array([0, 100, 100]) upper_red = ...

import cv2 import numpy as np img_path = 'C:\\Users\\11021\\Desktop\\火1二辉橄榄岩1-2.jpg' img = cv2.imread(img_path) # 定义每种矿物质的HSV范围 mineral_ranges = { 'biotite': [(0, 0, 50), (10, 255, 255)], # 黑云母 'hornblende': [(30, 50, 50), (50, 255, 255)], # 角闪石 'olivine': [(50, 50, 50), (70, 255, 255)], # 橄榄石 'plagioclase': [(100, 50, 50), (120, 255, 255)], # 斜长石 'pyroxene': [(70, 50, 50), (90, 255, 255)], # 辉石 'quartz': [(20, 50, 50), (30, 255, 255)] # 石英 } # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 创建掩膜和标签数组 masks = [] labels = np.zeros_like(hsv[:, :, 0]) # 遍历每种矿物质的范围，并创建掩膜 for i, (mineral, (lower, upper)) in enumerate(mineral_ranges.items()): mask = cv2.inRange(hsv, np.array(lower), np.array(upper)) masks.append(mask) labels[mask == 255] = i # 保存标签数据到文件 np.savetxt('labels.txt', labels)

接着，使用cv2.imread()函数读取指定路径的图像，并将其存储在img变量中。然后，使用cv2.cvtColor()函数将图像从BGR格式转换为HSV格式，并将结果存储在hsv变量中。接下来，代码遍历每种矿物质的范围，并使用cv2.in...

import cv2 import numpy as np def cvtBackground(path,color): """ 功能：给证件照更换背景色(常用背景色红、白、蓝) 输入参数：path:照片路径 color:背景色 """ im=cv2.imread(path) im_hsv=cv2.cvtColor(im,cv2.COLOR_BGR2HSV) #BGR和HSV的转换使用 cv2.COLOR_BGR2HSV #aim=np.uint8([[im[0,0,:]]]) #hsv_aim=cv2.cvtColor(aim,cv2.COLOR_BGR2HSV) mask=cv2.inRange(im_hsv,np.array([im_hsv[0,0,0]-0.1,100,100]),np.array([im_hsv[0,0,0]+0.1,255,255])) #利用cv2.inRange函数设阈值，去除背景部分 mask1=mask #在lower_red～upper_red之间的值变成255 img_median = cv2.medianBlur(mask,5) #自己加，中值滤波，去除一些边缘噪点 mask2 = img_median mask_inv=cv2.bitwise_not(mask2) img1=cv2.bitwise_and(im,im,mask=mask_inv) #将人物抠出 bg=im.copy() rows,cols,channels=im.shape bg[:rows,:cols,:]=color img2=cv2.bitwise_and(bg,bg,mask=mask2) #将背景底板抠出 img=cv2.add(img1,img2) #改变图片比例 h, w = img.shape[:2] img5 = cv2.resize(img, (int(w * 1/3), int(h * 1/3)), interpolation=cv2.INTER_LINEAR) image={'im':im,'im_hsv':im_hsv,'mask':mask1,'img_median':img_median,'img':img5} cv2.startWindowThread() #加了这个后在图片窗口按Esc就可以关闭图片窗口 for key in image: cv2.namedWindow(key) cv2.imshow(key,image[key]) cv2.waitKey(0) cv2.destroyAllWindows() return image #test if name=='main': img=cvtBackground('D:\picture\\rm.jpg',[255,255,255])设计思路

1. 读取照片，将BGR格式转换为HSV格式； 2. 设定阈值，将背景部分的像素点设为0，人物部分的像素点设为255，得到背景掩膜； 3. 对背景掩膜进行中值滤波，去除一些边缘噪点； 4. 利用背景掩膜和人物掩膜，将人物抠...

以下代码出现错误：NameError: name 'left_image' is not defined。代码如下：@pyqtSlot() def on_pushButton_5_clicked(self): # 读取左相机图像 left_image_path = '1_left.JPG' # 替换为实际图像的路径 left_image = cv2.imread(left_image_path) # 转换为HSV颜色空间 hsv_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2HSV) # 定义红色的HSV颜色范围 lower_red = np.array([0, 100, 100]) upper_red = np.array([10, 255, 255]) # 对图像进行红色阈值处理 red_mask = cv2.inRange(hsv_image, lower_red, upper_red) # 执行形态学操作，去除噪声 kernel = np.ones((5, 5), np.uint8) red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel) # 查找红色轮廓 contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 保留最大的两个轮廓 contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2] # 遍历轮廓并绘制圆心和坐标 for contour in contours: # 计算轮廓的最小外接圆 (x, y), radius = cv2.minEnclosingCircle(contour) center = (int(x), int(y)) radius = int(radius) # 绘制圆心 cv2.circle(left_image, center, 3, (0, 255, 0), -1) # 绘制圆形轮廓 cv2.circle(left_image, center, radius, (0, 0, 255), 2) # 绘制坐标 text = f'({int(x)}, {int(y)})' cv2.putText(left_image, text, (int(x) + 10, int(y) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) cv2.imshow('Result', left_image) cv2.waitKey(0) showImg = cv2.cvtColor('image', cv2.COLOR_BGR2RGB) qImgae = QImage(showImg, showImg.shape[1], showImg.shape[0], showImg.shape[1]*3, QImage.Format_RGB888) self.label.setPixmap(QPixmap(qImgae).scaled(self.label.width(), self.label.height(), Qt.KeepAspectRatio))

left_image = cv2.imread(left_image_path) # 转换为HSV颜色空间 hsv_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2HSV) # 定义红色的HSV颜色范围 lower_red = np.array([0, 100, 100]) upper_red = np....

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