img_bgr = show_circle_color(img_bgr, red, treasure2) File "D:\23101\比赛\光电赛\maze_car\maze.py", line 176, in show_circle_color img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) cv2.error: OpenCV(4.7.0) d:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function '__cdecl cv::impl::`anonymous-namespace'::CvtHelper<struct cv::impl::`anonymous namespace'::Set<1,-1,-1>,struct cv::impl::A0x981fb336::Set<3,4,-1>,struct cv::impl::A0x981fb336::Set<0,2,5>,2>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)' > Invalid number of channels in input image: > 'VScn::contains(scn)' > where > 'scn' is 3

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colsys.zip_COLSYS_GBR_beautiful_rgb BGR_rgb to bgr

This code converts regular RGB images into five other color systems--RBG,GRB,GBR,BRG,BGR. Changing the color systems gives a different experience of how things could look even more beautiful in ...

import numpy as np import cv2 import matplotlib.pyplot as plt # 读取图片 pic_file='./photo/cat.jpg' img_bgr = cv2.imread(pic_file,cv2.IMREAD_COLOR) cv2.imshow("input",img_bgr) # 分别获取三个通道的ndarray数据 img_b=img_bgr[:,:,0] img_g=img_bgr[:,:,1] img_r=img_bgr[:,:,2] '''按R、G、B三个通道分别计算颜色直方图''' b_hist = cv2.calcHist([img_bgr],[0],None,[256],[0,255]) g_hist = cv2.calcHist([img_bgr],[1],None,[256],[0,255]) r_hist = cv2.calcHist([img_bgr],[2],None,[256],[0,255]) m,dev = cv2.meanStdDev(img_bgr) #计算G、B、R三通道的均值和方差 # img_r_mean=np.mean(r_hist) #计算R通道的均值 # print(m) # print(dev) '''显示三个通道的颜色直方图''' plt.plot(b_hist,label='B',color='blue') plt.plot(g_hist,label='G',color='green') plt.plot(r_hist,label='R',color='red') plt.legend(loc='best') plt.xlim([0,256]) plt.show() cv2.waitKey(0)

您可以使用OpenCV库中的cv2.imread函数读取图片，并将其分成三个通道（蓝色、绿色和红色）。然后，使用cv2.calcHist函数计算每个通道的颜色直方图。最后，使用matplotlib.pyplot库绘制每个通道的颜色直方图。请...

img_bgr_data = cv2.imread('./images/messi.jpg') img_hsv_data = cv2.cvtColor(img_bgr_data, cv2.COLOR_BGR2HSV) cv2.imshow('bgr', img_bgr_data) cv2.imshow('hsv', img_hsv_data) cv2.waitKey(0) cv2.destroyAllWindows()

这是一段使用 OpenCV 库读取和显示图像的 Python 代码。它将读取名为 messi.jpg 的图像文件，并将其转换为 HSV 颜色空间，然后在两个窗口中显示原始图像和转换后的图像。最后，它将等待用户按下任意键，然后关闭所有...

img_gray_data = cv2.imread('./images/messi.jpg', cv2.IMREAD_GRAYSCALE) img_bgr_data = cv2.cvtColor(img_gray_data, cv2.COLOR_GRAY2BGR) cv2.imshow('gray', img_gray_data) cv2.imshow('bgr', img_bgr_data) cv2.waitKey(0) cv2.destroyAllWindows()

其中，cv2.imread() 函数用于读取图像，cv2.cvtColor() 函数用于将灰度图像转换为 BGR 彩色图像，cv2.imshow() 函数用于显示图像，cv2.waitKey() 函数用于等待用户按下键盘，cv2.destroyAllWindows() 函数用于关闭...

""" Contrast Limited Adaptive Histogram Equalization,CLAHE 对比度受限自适应直方图均衡 """ import cv2 # import numpy as np import matplotlib.pyplot as plt def show_img_with_matplotlib(color_img, title, pos): img_rgb = color_img[:, :, ::-1] plt.subplot(2, 5, pos) plt.imshow(img_rgb) plt.title(title, fontsize=8) plt.axis('off') def equalize_clahe_color_hsv(img): cla = cv2.createCLAHE(clipLimit=4.0) H, S, V = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) eq_V = cla.apply(V) eq_image = cv2.cvtColor(cv2.merge([H, S, eq_V]), cv2.COLOR_HSV2BGR) return eq_image def equalize_clahe_color_lab(img): cla = cv2.createCLAHE(clipLimit=4.0) L, a, b = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2Lab)) eq_L = cla.apply(L) eq_image = cv2.cvtColor(cv2.merge([eq_L, a, b]), cv2.COLOR_Lab2BGR) return eq_image def equalize_clahe_color_yuv(img): cla = cv2.createCLAHE(clipLimit=4.0) Y, U, V = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2YUV)) eq_Y = cla.apply(Y) eq_image = cv2.cvtColor(cv2.merge([eq_Y, U, V]), cv2.COLOR_YUV2BGR) return eq_image def equalize_clahe_color(img): cla = cv2.createCLAHE(clipLimit=4.0) channels = cv2.split(img) eq_channels = [] for ch in channels: eq_channels.append(cla.apply(ch)) eq_image = cv2.merge(eq_channels) return eq_image # 加载图像 image = cv2.imread('D:/Documents/python/OpenCV/image/008.jpg') gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 灰度图像应用 CLAHE clahe = cv2.createCLAHE(clipLimit=2.0) gray_image_clahe = clahe.apply(gray_image) # 使用不同 clipLimit 值 clahe.setClipLimit(5.0) gray_image_clahe_2 = clahe.apply(gray_image) clahe.setClipLimit(10.0) gray_image_clahe_3 = clahe.apply(gray_image) clahe.setClipLimit(20.0) gray_image_clahe_4 = clahe.apply(gray_image) # 彩色图像应用 CLAHE image_clahe_color = equalize_clahe_color(image) image_clahe_color_lab = equalize_clahe_color_lab(image) image_clahe_color_hsv = equalize_clahe_color_hsv(image) image_clahe_color_yuv = equalize_clahe_color_yuv(image) # 标题 plt.figure(figsize=(10, 4)) plt.suptitle("Color histogram equalization with cv2.equalizedHist() - not a good approach", fontsize=9, fontweight='bold') # 可视化 show_img_with_matplotlib(cv2.cvtColor(gray_image, cv2.COLOR_GRAY2BGR), "gray", 1) show_img_with_matplotlib(cv2.cvtColor(gray_image_clahe, cv2.COLOR_GRAY2BGR), "gray CLAHE clipLimit=2.0", 2) show_img_with_matplotlib(cv2.cvtColor(gray_image_clahe_2, cv2.COLOR_GRAY2BGR), "gray CLAHE clipLimit=5.0", 3) show_img_with_matplotlib(cv2.cvtColor(gray_image_clahe_3, cv2.COLOR_GRAY2BGR), "gray CLAHE clipLimit=10.0", 4) show_img_with_matplotlib(cv2.cvtColor(gray_image_clahe_4, cv2.COLOR_GRAY2BGR), "gray CLAHE clipLimit=20.0", 5) show_img_with_matplotlib(image, "color", 6) show_img_with_matplotlib(image_clahe_color, "clahe on each channel(BGR)", 7) show_img_with_matplotlib(image_clahe_color_lab, "clahe on each channel(LAB)", 8) show_img_with_matplotlib(image_clahe_color_hsv, "clahe on each channel(HSV)", 9) show_img_with_matplotlib(image_clahe_color_yuv, "clahe on each channel(YUV)", 10) plt.show()

CLAHE，即对比度受限自适应直方图均衡化，是一种用于增强图像对比度的方法。在计算图像直方图均衡化的过程中，CLAHE会先将图像分成许多小块，并对每个...在OpenCV库中，可以通过cv2.createCLAHE()函数来调用CLAHE算法。

import cv2 import numpy as np import tensorflow as tf # 加载之前训练好的模型 model = tf.keras.models.load_model('mnist_cnn_model') for img in images_data: # 将RGB格式转换为BGR格式 img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # 转换为灰度图像 gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) # 二值化处理 _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) # 找到轮廓 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 初始化计数器 count = 0 # 遍历所有轮廓 for contour in contours: # 计算轮廓面积 area = cv2.contourArea(contour) if area < 200 or area > 2000: # 如果轮廓面积小于10个像素，则忽略该轮廓 continue # 获取轮廓的外接矩形 x, y, w, h = cv2.boundingRect(contour) # 在原始图像上标记出抠出来的数字部分，并将BGR格式转换为RGB格式 cv2.rectangle(img_bgr, (x, y), (x+w, y+h), (0, 255, 0), 2) digit = cv2.cvtColor(img_bgr[y:y+h, x:x+w], cv2.COLOR_BGR2RGB) # 对数字图像进行预处理，使其与训练数据具有相同的格式 digit_resized = cv2.resize(digit, (28, 28)) digit_gray = cv2.cvtColor(digit_resized, cv2.COLOR_RGB2GRAY) digit_normalized = digit_gray / 255.0 digit_reshaped = np.reshape(digit_normalized, (1, 28, 28)) # 进行预测并输出最大概率对应的数字 prediction = model.predict(digit_reshaped) digit_class = np.argmax(prediction) print("抠出来的数字是:", digit_class) # 增加计数器 count += 1 # 在原始图像上显示标记过的抠出来的数字部分 #plt.figure(figsize=(20, 20)) #plt.imshow(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)) #plt.show() # 显示抠出来的数字个数 print("抠出来了{}个数字".format(count)) 如何解决error Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_12928/408549256.py in <module> 8 for img in images_data: 9 # 将RGB格式转换为BGR格式 ---> 10 img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) 11 12 # 转换为灰度图像 error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'cvtColor' > Overload resolution failed: > - src is not a numpy array, neither a scalar > - Expected Ptrcv::UMat for argument 'src'这个错误

根据错误提示，cvtColor函数的src参数不是一个numpy数组或标量。检查一下images_data中的图片格式是否正确。images_data中的图片应该是numpy数组格式，如果不是，你需要将其转换为numpy数组格式才能进行后续...

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...

import qrcode import cv2 import numpy as np from PIL import Image # 要生成二维码的字符串 data = "你好，世界！" # 生成二维码 qr = qrcode.QRCode( version=None, error_correction=qrcode.constants.ERROR_CORRECT_L, box_size=10, border=4, ) qr.add_data(data) qr.make(fit=True) qr_img = qr.make_image(fill_color="black", back_color="white") # 将二维码转化为OpenCV格式 qr_img_cv = cv2.cvtColor(np.asarray(qr_img), cv2.COLOR_RGB2BGR) # 打开动态背景图片 cap = cv2.VideoCapture("bg.mp4") # 循环读取视频帧并加入二维码 while True: ret, frame = cap.read() if not ret: break # 将带有二维码的图片缩小并粘贴到视频帧上 qr_img_cv_resized = cv2.resize(qr_img_cv, (200, 200)) frame[100:300, 100:300] = qr_img_cv_resized # 显示视频帧 cv2.imshow("frame", frame) if cv2.waitKey(1) == ord('q'): break # 释放资源 cap.release() cv2.destroyAllWindows()这段代码中报错Traceback (most recent call last): File "D:/Phoenix Robot/python/代码/test1.py", line 21, in <module> qr_img_cv = cv2.cvtColor(np.asarray(qr_img), cv2.COLOR_RGB2BGR) TypeError: Expected Ptr<cv::UMat> for argument '%s'怎么解决

qr_img_cv = cv2.cvtColor(cv2.UMat(np.asarray(qr_img)), cv2.COLOR_RGB2BGR) 或者直接将 qr_img 转换为 cv::Mat 类型： python qr_img_cv = cv2.cvtColor(cv2.Mat(np.asarray(qr_img)), cv2.COLOR_...

解释代码 img_input = cv2.cvtColor(img_input,cv2.COLOR_GRAY2BGR)

cv2.COLOR_GRAY2BGR 是 OpenCV 中的一个常量，它表示从灰度图像到BGR彩色图像的颜色通道转换方式。在该代码中，使用 cv2.cvtColor() 函数将输入的灰度图像 img_input 转换为 BGR 彩色图像，并将结果赋值给 img_input...

img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) 取出一个像素点分解成 BGR

img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) # 取出(100, 100)处的像素点 b, g, r = img_np[100, 100] print("B: ", b) print("G: ", g) print("R: ", r) 其中，可以使用numpy数组的索引操作取出某个...

Traceback (most recent call last): File "D:\pythonProject1\testwork\caridentify4.py", line 169, in <module> gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) NameError: name 'card_img' is not defined

这个错误提示说明在执行代码时，变量"card_img"没有被定义。你需要先定义这个变量并给它赋值，...gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) 其中，'your_image_path.jpg'是你要读取的图像的路径。

card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) cv2.error: OpenCV(4.4.0) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-qjdp5db9\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'

card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) 改为： python card_img_hsv = cv2.cvtColor(card_img, cv2.BGR2HSV) 如果问题仍然存在，请提供更多的代码和错误信息，以便更好地帮助您...

解释代码 original_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2BGR) cv2.imwrite(diff_img_path, original_img)

首先，将原始图像original_img从RGB格式转换为BGR格式，使用cv2.cvtColor函数，将其作为第一个参数，将转换模式cv2.COLOR_RGB2BGR作为第二个参数，将转换后的图像赋值给original_img，这样就可以将图像从RGB格式转换...

解释 try: img = cv2.imdecode(np.fromfile(self.pic_path, dtype=np.uint8), cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, (480, 320))

- cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 使用 OpenCV 库的 cvtColor() 函数将图片从 BGR 色彩空间转换到 RGB 色彩空间。由于 OpenCV 默认使用的是 BGR 色彩空间，而 Matplotlib 库使用的是 RGB 色彩空间，因此...

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img_bgr_data = cv2.imread('./images/messi.jpg') img_hsv_data = cv2.cvtColor(img_bgr_data, cv2.COLOR_BGR2HSV) cv2.imshow('bgr', img_bgr_data) cv2.imshow('hsv', img_hsv_data) cv2.waitKey(0) cv2.destroyAllWindows()

img_gray_data = cv2.imread('./images/messi.jpg', cv2.IMREAD_GRAYSCALE) img_bgr_data = cv2.cvtColor(img_gray_data, cv2.COLOR_GRAY2BGR) cv2.imshow('gray', img_gray_data) cv2.imshow('bgr', img_bgr_data) cv2.waitKey(0) cv2.destroyAllWindows()

img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img_gray.shape

解释代码 img_input = cv2.cvtColor(img_input,cv2.COLOR_BGR2GRAY)

img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img_hsi = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)

解释代码 img_input = cv2.cvtColor(img_input,cv2.COLOR_GRAY2BGR)

img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) 取出一个像素点 分解成 BGR

Traceback (most recent call last): File "D:\pythonProject1\testwork\caridentify4.py", line 169, in <module> gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) NameError: name 'card_img' is not defined

card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) cv2.error: OpenCV(4.4.0) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-qjdp5db9\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'

解释代码 original_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2BGR) cv2.imwrite(diff_img_path, original_img)

解释 try: img = cv2.imdecode(np.fromfile(self.pic_path, dtype=np.uint8), cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, (480, 320))

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