python+opencv寻找图片或视频中颜色进行追踪之HSV颜色处理

python+opencv寻找图片或视频中颜色进行追踪之HSV颜色处理

1.颜色空间转换

import cv2img = cv2.imread('1.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)

cv2.cvtColor()用来进行颜色模型转换，参数 1 是要转换的图片，参数 2 是转换模式

2.视频中特定颜色物体追踪

import cv2
import numpy as npdef detect_white(image_path):# 读取图像image = cv2.imread(image_path)# 将图像从RGB转换为HSVhsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)# 定义白色在HSV颜色空间的阈值范围lower_white = np.array([114,  36, 234], dtype=np.uint8)upper_white = np.array([177 , 10 ,251], dtype=np.uint8)# 应用阈值，提取白色区域white_mask = cv2.inRange(hsv_image, lower_white, upper_white)# 对提取的二值图像进行形态学操作，去除噪音kernel = np.ones((5, 5), np.uint8)white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_OPEN, kernel)# 在原始图像上标记白色区域result = cv2.bitwise_and(image, image, mask=white_mask)# 显示结果图像cv2.imshow("Result", result)cv2.waitKey(0)cv2.destroyAllWindows()# 调用函数进行白色区域识别
detect_white("2.jpg")

3.那蓝色的 HSV 值的上下限 lower 和 upper 范围是怎么得到的呢？

import cv2
import numpy as np# img是你的BGR图像
img = cv2.imread('1111.png')# 将BGR图像转换为HSV图像
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# 获取H, S, V的最小值和最大值
min_h, max_h = np.min(hsv_img[:,:,0]), np.max(hsv_img[:,:,0])
min_s, max_s = np.min(hsv_img[:,:,1]), np.max(hsv_img[:,:,1])
min_v, max_v = np.min(hsv_img[:,:,2]), np.max(hsv_img[:,:,2])print(min_h, max_h)
print(min_s, max_s)
print(min_v, max_v)

或者：

import cv2img = cv2.imread('2.jpg')
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)def mouse_click(event, x, y, flags, para):if event == cv2.EVENT_LBUTTONDOWN:  # 左边鼠标点击print('PIX:', x, y)# print("BGR:", img[y, x])# print("GRAY:", gray[y, x])print("HSV:", hsv[y, x])if __name__ == '__main__':cv2.namedWindow("img")cv2.setMouseCallback("img", mouse_click)while True:cv2.imshow('img', img)if cv2.waitKey() == ord('q'):break