Zhang Phil

OpenCV方向梯度直方图HOG视频中运动人体检测，Python假设需要把一段视频中的处于运动（活动）的人体检测出来并用框将人框起来：from imutils.object_detection import non_max_suppressionimport numpy as npimport imutilsimport cv2hog = cv2.HOGDescriptor()hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDe

OpenCV图像二值化，Python原图：二值化处理：import cv2if __name__=="__main__": image = cv2.imread("pic.jpg") gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #超过阈值thresh，值为255，未超过为0。 ret1, p1 = cv2.threshold(src=gray, thresh=127, maxval=255, ty.

OpenCV图像寻找特定颜色像素点坐标，并用不同颜色描点填充，Python假设现在有一张九宫格图片：需要把图中的1到9数组用金黄色颜色描点填充。import cv2import numpy as npif __name__=="__main__": img = cv2.imread("pic.jpg") hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 色彩空间转换为hsv，分离. #色相（H）是色彩的基本属性，

def rgb2hsv(r, g, b): r, g, b = r/255.0, g/255.0, b/255.0 mx = max(r, g, b) mn = min(r, g, b) m = mx-mn if mx == mn: h = 0 elif mx == r: if g >= b: h = ((g-b)/m)*60 else: h = ((g-b.

假设现在有一个源视频文件test.mp4，把watermark.jpg作为水印图片logo加入到视频test.mp4中，最终生成添加水印后的result.mp4文件：import moviepy.editor as mpif __name__ == "__main__": video = mp.VideoFileClip("test.mp4") logo = (mp.ImageClip("watermark.jpg") .set_duration(vi.

#OpenCV对视频录像按照间隔切割抽取图片帧，Pythonimport cv2import osif __name__ == "__main__": vc = cv2.VideoCapture('test.mp4') if vc.isOpened(): # 是否正常打开 print("打开ok") else: print("打开失败，程序退出") exit(-1) savedpath = 'image/' .

import cv2import osif __name__ == "__main__": vc = cv2.VideoCapture('test.mp4') if vc.isOpened(): # 是否正常打开 print("打开ok") else: print("打开失败，程序退出") exit(-1) savedpath = 'image/' isExists = os.path.exists(sav.

假设有一张九宫格图片：需要忽略里面的1到9数字，仅仅把框架提取出来，那么就需要用到HSV色域空间转换：import numpy as npimport cv2if __name__ == "__main__": img = cv2.imread("p.jpg") hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 色彩空间转换为hsv，分离. low = np.array([0, 100, 100]) high =

假设现在把图中的足球圆形轮廓检测出来，并用红线标记：python代码：import cv2import numpy as npif __name__ == "__main__": original_img = cv2.imread("c.jpg") gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY) blur_img = cv2.GaussianBlur(gray, (3, 3), 0) ci.

假设需要把图片中的直线检测出来并用红线标记：import cv2import numpy as npif __name__ == "__main__": original_img = cv2.imread("p.jpg") gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY) blur_img = cv2.GaussianBlur(gray, (3, 3), 0) edges = cv2.Canny(b

假设一张这样的图片：检测边缘轮廓：# OpenCV Canny边缘轮廓检测，Pythonimport cv2if __name__ == "__main__": img = cv2.imread("pic.jpg") # 高斯模糊 gaussian_img = cv2.GaussianBlur(img, (3, 3), 0) result = cv2.Canny(gaussian_img, 200, 480) cv2.imwrite("canny

OpenCV图像相似度检测，简单的说是大图中找小图。假设把大图中的一部分图Android小机器人检测出来并用红色的线框出来。# OpenCV图像相似度检测matchTemplate，Python实现import cv2 as cvif __name__ == "__main__": # 先把图片灰度处理。 img = cv.imread('pic.jpg') img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRA.

Python裁剪截取图片两种方案：OpenCV与Pillow# Python裁剪截取图片两种方案：OpenCV与Pillowimport cv2from PIL import ImagesrcImg = "./image.jpg"x = 8y = 16w = 32h = 16# OpenCV的方法def method_1(): img = cv2.imread(srcImg) print(img.shape) # 提供矩形的高度区间和宽度区间