可以使用下面的色彩空间转化函数 cv2.cvtColor( )进行色彩空间的转换:HSV 表示hue、saturation、valueimage_hsv = cv2.cvtColor(image,cv2
可以使用opencv中cv2.cvtColor()函数来改变图像的颜色空间,该函数形式为: cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) @frame为要进行处理的图片;
API Definition 我们从 OpenCV官网 的Miscellaneous Image Transformations 上,可查到 cv2.cvtColor 这个api的定义如下:...C++: void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0 ) Python: cv2.cvtColor(src...space conversion),均是 int 型的: 4 6 颜色空间转换探究 于是我另外编写了一小段代码,探究哪些整数可以作为 cv2...(自己写的实验源码附在文章末尾) 验证得知,以下整数可以作为 cv2.cvtColor 中 code 参数的 替代输入值: Valid index in cv2.cvtColor: [0, 1, 2,...'), img_new) valid_index.append(color_type) except: pass print ('Valid index in cv2
如下代码所示: # 读取图片 img = cv2.imread('example.jpg') print(f'type: {type(img)}') plt.axis('off') plt.imshow(cv2...matplotlib.pyplot as plt %matplotlib inline 然后直接调用 plt.imshow() 函数,不过 opencv 都需要做一个转换过程,即: plt.imshow(cv2....cvtColor(img, cv2.COLOR_BGR2GRAY) plt.figure(figsize=(32, 32)) plt.subplot(1, 2, 1) plt.imshow(cv2....实现代码: gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) moment = cv2.moments(gray_img) X = int(moment...实现代码: img1 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) circles
matplotlib.pyplot as plt img=cv2.imread('C:/Users/xpp/Desktop/Lena.png')#读取图像 h,w=img.shape[:2] yvu=cv2...ru=cv2.resize(u,(w,h))#恢复采样前的u分量,用这个u分量来重建图片文件 rv=cv2.resize(v,(w,h)) yvu=cv2.merge((y,rv,ru)) bgr=cv2...(yvu_pre,cv2.COLOR_YCrCb2BGR)#将y的预测误差转换为图片 yvu_re=cv2.merge((img_re,rv,ru)) bgr_re=cv2.cvtColor(yvu_re...['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus']=False plt.subplot(241),plt.imshow(cv2...(ru,cv2.COLOR_BGR2RGB),cmap='gray'), plt.title('u分量'),plt.axis('off') plt.subplot(246),plt.imshow(cv2
.cvtColor(img, cv2.COLOR_BGR2RGB)), plt.title("Original") plt.subplot(122), plt.imshow(cv2.cvtColor(dst....cvtColor(img, cv2.COLOR_BGR2RGB)), plt.title("Origin") plt.subplot(122), plt.imshow(cv2.cvtColor(dst....cvtColor(img, cv2.COLOR_BGR2RGB)), plt.title(r"$Origin$") plt.subplot(222), plt.imshow(cv2.cvtColor(...imgR90, cv2.COLOR_BGR2RGB)), plt.title(r"$Rotation 90^{o}$") plt.subplot(223), plt.imshow(cv2.cvtColor....cvtColor(img, cv2.COLOR_BGR2RGB)), plt.title("imgOrigin") plt.subplot(122), plt.imshow(cv2.cvtColor(
目标 颜色空间转换,如BGR↔Gray,BGR↔HSV等 追踪视频中特定颜色的物体 OpenCV函数:cv2.cvtColor(), cv2.inRange() 教程 颜色空间转换 import cv2...img = cv2.imread('lena.jpg') # 转换为灰度图 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cv2.imshow(...'img', img) cv2.imshow('gray', img_gray) cv2.waitKey(0)Copy to clipboardErrorCopied cv2.cvtColor()用来进行颜色模型转换...小结 cv2.cvtColor()函数用来进行颜色空间转换,常用BGR↔Gray,BGR↔HSV。 HSV颜色模型常用于颜色识别。...(效果如下) 接口文档 cv2.cvtColor() cv2.inRange() cv2.bitwise_and() 引用 本节源码 Changing Colorspaces
OpenCV图片格式转换成PIL的图片格式; 使用PIL绘制文字; PIL图片格式转换成OpenCV的图片格式; 代码分解 OpenCV图片转换为PIL图片格式 img = Image.fromarray(cv2...PIL图片格式转换成OpenCV的图片格式 cv2.cvtColor(numpy.asarray(img), cv2.COLOR_RGB2BGR) 完整代码 封装好的完整方法 #coding=utf-8...textSize=20): if (isinstance(img, numpy.ndarray)): #判断是否OpenCV图片类型 img = Image.fromarray(cv2..., textSize, encoding="utf-8") draw.text((left, top), text, textColor, font=fontText) return cv2
value) w*h*3 HSV.png HLS.png Gray = w*h*[0-255] Binary = w*h*[0/1] # RGB/BGR HSL/HSV/GRAY cv2....cvtColor(image, cv2.COLOR_BGR2RGB) cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.cvtColor(image, cv2.COLOR_BGR2HSL...gray, 127, 255, cv2.THRESH_TOZERO_INV) # Binary -> Gray -> BGR np.array(binary*255).astype(np.uint8) cv2....cvtColor(gray, cv2.COLOR_GRAY2BGR) # HSV/HSL color filter hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV
img) 可以写一个完整的测试代码如下 import matplotlib.pyplot as plt import cv2 img = cv2.imread("OIP.jpg") img = cv2..., cv2.COLOR_BGR2GRAY) plt.hist(img.ravel(), bins=256) plt.title('origin') plt.show() # 原始直方图 img = cv2....cvtColor(img, cv2.COLOR_BGR2RGB) plt.title('origin') plt.imshow(img) plt.show() # 原始灰度图 img = cv2....plt.show() # 均衡化灰度图 在这里我们手动实现一个图像的直方图均衡化,不调用库函数 首先读取一张照片并将其转化为灰度图 img = cv2.imread("OIP.jpg") img = cv2...完整代码如下 import matplotlib.pyplot as plt import cv2 import numpy img = cv2.imread("OIP.jpg") img = cv2
images image_stripes = cv2.imread('images/stripes.jpg') # Change color to RGB (from BGR) image_stripes = cv2...images image_solid = cv2.imread('images/pink_solid.jpg') # Change color to RGB (from BGR) image_solid = cv2..., cv2.COLOR_RGB2GRAY) gray_solid = cv2.cvtColor(image_solid, cv2.COLOR_RGB2GRAY) # normalize the image...# Read in an image image = cv2.imread('images/birds.jpg') # Change color to RGB (from BGR) image = cv2....cvtColor(image, cv2.COLOR_BGR2RGB) # convert to grayscale gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY
plt.axis('off') plt.imshow(image) plt.show() image = cv2.imread('assets/cvtutorials.png') image = cv2...GENERATED_PICS_SIZE = 2 # 可以设置想生成的图片个数 RES_PATH_PREFIX = "res" image = cv2.imread('assets/cvtutorials.png') image = cv2...A.GridDistortion(), ]) augmented_image = transform(image=image)['image'] image = cv2...pic_path_prefix = pic_path.split('.')[0].split('\\')[-1] image = cv2.imread(pic_path) image = cv2...A.GridDistortion(), ]) augmented_image = transform(image=image)['image'] image = cv2
轮廓检测是形状分析和物体检测和识别的有用工具,连接所有连续点(沿着边界)的曲线,具有相同的颜色或强度,效果如下所示: 代码如下所示: if values['contour']: hue = cv2..., values['blur_slider']) 4.5、色彩转换 色彩空间的转化,HSV转换为BGR,效果如下所示: 代码如下所示: if values['hue']: frame = cv2....cvtColor(frame, cv2.COLOR_BGR2HSV) frame[:, :, 0] += int(values['hue_slider']) frame = cv2.cvtColor...enhance_slider'] / 40 clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) lab = cv2....cvtColor(frame, cv2.COLOR_BGR2LAB) lab[:, :, 0] = clahe.apply(lab[:, :, 0]) frame = cv2.cvtColor
)#实现仿射变换 plt.figure(figsize=(9,6)) plt.subplot(221),plt.axis('off'),plt.title("T1:Zoom") plt.imshow(cv2...imgT1, cv2.COLOR_BGR2RGB)), plt.subplot(222),plt.axis('off'),plt.title("T2:Translation") plt.imshow(cv2...cvtColor(imgT2, cv2.COLOR_BGR2RGB)) plt.subplot(223),plt.axis('off'),plt.title("T3:Rotation") plt.imshow(cv2...cvtColor(imgT3, cv2.COLOR_BGR2RGB)) plt.subplot(224),plt.axis('off'),plt.title("T4:Shear") plt.imshow(cv2
图片格式转换成PIL的图片格式; 使用PIL绘制文字; PIL图片格式转换成OpenCV的图片格式; 代码分解 **OpenCV图片转换为PIL图片格式** img = Image.fromarray(cv2...**PIL图片格式转换成OpenCV的图片格式** cv2.cvtColor(numpy.asarray(img), cv2.COLOR\_RGB2BGR) 完整代码 封装好的完整方法 #coding=...textSize=20): if (isinstance(img, numpy.ndarray)): #判断是否OpenCV图片类型 img = Image.fromarray(cv2...textSize, encoding="utf-8") draw.text((left, top), text, textColor, font=fontText) return cv2
255, 0), textSize=20): if (isinstance(img, np.ndarray)): #判断是否为OpenCV图片类型 img = Image.fromarray(cv2...encoding="utf-8") ##中文字体 draw.text((left, top), text, textColor, font=fontText) #写文字 return cv2...得到轮廓 cnt = contours[0] #取出轮廓 x, y, w, h = cv2.boundingRect(cnt) #用一个矩形将轮廓包围 img_gray = cv2...255, 0), textSize=20): if (isinstance(img, np.ndarray)): #判断是否为OpenCV图片类型 img = Image.fromarray(cv2...encoding="utf-8") ##中文字体 draw.text((left, top), text, textColor, font=fontText) #写文字 return cv2
希望文章对您有所帮助,如果有不足之处,还请海涵~ 本篇文章讲解图像灰度化处理的知识,结合OpenCV调用cv2.cvtColor()函数实现图像灰度操作,使用像素处理方法对图像进行灰度化处理。...其函数原型如下所示: dst = cv2.cvtColor(src, code[, dst[, dstCn]]) src表示输入图像,需要进行颜色空间变换的原图像 dst表示输出图像,其大小和深度与src...同样,可以调用 grayImage = cv2.cvtColor(src, cv2.COLOR_BGR2HSV) 核心代码将彩色图像转换为HSV颜色空间,如下图所示。...BGR转HSV img_HSV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HSV) #BGR转YCrCb img_YCrCb = cv2.cvtColor(img_BGR...= cv2.cvtColor(img_BGR, cv2.COLOR_BGR2XYZ) #BGR转LAB img_LAB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2LAB
numpy as np from matplotlib import pyplot as plt img = cv2.imread(r'C:\Users\mx\Desktop\1.jpg') hsv = cv2....cvtColor(roi,cv2.COLOR_BGR2HSV) target = cv2.imread(r'C:\Users\mx\Desktop\gz\4.png') hsv_target = cv2...from matplotlib import pyplot as plt roi = cv2.imread(r'C:\Users\mx\Desktop\gz\roi.png') hsv_roi = cv2....cvtColor(roi,cv2.COLOR_BGR2HSV) target = cv2.imread(r'C:\Users\mx\Desktop\gz\4.png') hsv_target = cv2....cvtColor(roi,cv2.COLOR_BGR2HSV) target = cv2.imread(r'C:\Users\mx\Desktop\gz\4.png') hsv_target = cv2
代码如下所示: if values['thresh']: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)[:, :, 0] frame =...代码如下所示: if values['contour']: hue = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) hue = cv2.GaussianBlur...代码如下所示: if values['hue']: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) frame[:, :, 0] += int...(values['hue_slider']) frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR) 4.6、调节对比度 增强对比度,使图像中的细节看起来更加清晰...enhance_slider'] / 40 clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) lab = cv2
imwrite('resized_200x300.jpg', img_200x300) cv2.imwrite('bordered_300x300.jpg', img_300x300) img_hsv = cv2...turn_green_hsv = img_hsv.copy() turn_green_hsv[:, :, 0] = (turn_green_hsv[:, :, 0]+15) % 180 turn_green_img = cv2...colorless_hsv = img_hsv.copy() colorless_hsv[:, :, 1] = 0.5 * colorless_hsv[:, :, 1] colorless_img = cv2...colorless_img) darker_hsv = img_hsv.copy() darker_hsv[:, :, 2] = 0.5 * darker_hsv[:, :, 2] darker_img = cv2
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