green = np.uint8([[[0,255,0 ]]]) hsv_green = cv2.cvtColor(green,cv2.COLOR_BGR2HSV) print(hsv_green)改写为c++代码
时间: 2024-04-16 11:27:46 浏览: 27
以下是将Python代码改写为C++代码的示例:
```cpp
#include <opencv2/opencv.hpp>
int main() {
// 定义绿色的BGR值
cv::Mat green(1, 1, CV_8UC3, cv::Scalar(0, 255, 0));
// 将颜色从BGR空间转换为HSV空间
cv::Mat hsv_green;
cv::cvtColor(green, hsv_green, cv::COLOR_BGR2HSV);
// 打印结果
std::cout << hsv_green << std::endl;
return 0;
}
```
请注意,此示例假设您已经正确安装了OpenCV库,并且在C++代码中包含了适当的头文件和命名空间。您可能需要根据您的实际环境进行适当的调整和修改。
相关问题
import numpy as np import cv2 class ColorMeter(object): color_hsv = { # HSV,H表示色调(度数表示0-180),S表示饱和度(取值0-255),V表示亮度(取值0-255) # "orange": [np.array([11, 115, 70]), np.array([25, 255, 245])], "yellow": [np.array([11, 115, 70]), np.array([34, 255, 245])], "green": [np.array([35, 115, 70]), np.array([77, 255, 245])], "lightblue": [np.array([78, 115, 70]), np.array([99, 255, 245])], "blue": [np.array([100, 115, 70]), np.array([124, 255, 245])], "purple": [np.array([125, 115, 70]), np.array([155, 255, 245])], "red": [np.array([156, 115, 70]), np.array([179, 255, 245])], } def __init__(self, is_show=False): self.is_show = is_show self.img_shape = None def detect_color(self, frame): self.img_shape = frame.shape res = {} # 将图像转化为HSV格式 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) for text, range_ in self.color_hsv.items(): # 去除颜色范围外的其余颜色 mask = cv2.inRange(hsv, range_[0], range_[1]) erosion = cv2.erode(mask, np.ones((1, 1), np.uint8), iterations=2) dilation = cv2.dilate(erosion, np.ones((1, 1), np.uint8), iterations=2) target = cv2.bitwise_and(frame, frame, mask=dilation) # 将滤波后的图像变成二值图像放在binary中 ret, binary = cv2.threshold(dilation, 127, 255, cv2.THRESH_BINARY) # 在binary中发现轮廓,轮廓按照面积从小到大排列 contours, hierarchy = cv2.findContours( binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE ) if len(contours) > 0: # cv2.boundingRect()返回轮廓矩阵的坐标值,四个值为x, y, w, h, 其中x, y为左上角坐标,w,h为矩阵的宽和高 boxes = [ box for box in [cv2.boundingRect(c) for c in contours] if min(frame.shape[0], frame.shape[1]) / 10 < min(box[2], box[3]) < min(frame.shape[0], frame.shape[1]) / 1 ] if boxes: res[text] = boxes if self.is_show: for box in boxes: x, y, w, h = box # 绘制矩形框对轮廓进行定位 cv2.rectangle( frame, (x, y), (x + w, y + h), (153, 153, 0), 2 ) # 将绘制的图像保存并展示 # cv2.imwrite(save_image, img) cv2.putText( frame, # image text, # text (x, y), # literal direction cv2.FONT_HERSHEY_SIMPLEX, # dot font 0.9, # scale (255, 255, 0), # color 2, # border ) if self.is_show: cv2.imshow("image", frame) cv2.waitKey(1) # cv2.destroyAllWindows() return res if __name__ == "__main__": cap = cv2.VideoCapture(0) m = ColorMeter(is_show=True) while True: success, frame = cap.read() res = m.detect_color(frame) print(res) if cv2.waitKey(1) & 0xFF == ord('q'): break
"red": (0, 255, 255),
"green": (85, 255, 128),
"blue": (170, 255, 128) } 你好!我能够理解你正在询问的是如何使用HSV色彩空间来表示不同的颜色。例如,红色的HSV值为(0,255,255),绿色的HSV值为(85,255,128),蓝色的HSV值为(170,255,128)。
简单方法识别红绿灯 Python opencv
以下是一种简单的方法来识别红绿灯。它使用Python和OpenCV库。
步骤1:导入库
```
import cv2
import numpy as np
```
步骤2:读取图像
```
img = cv2.imread('image.jpg')
```
步骤3:将图像转换为HSV颜色空间
```
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
```
步骤4:定义颜色范围
```
lower_red = np.array([0, 50, 50])
upper_red = np.array([10, 255, 255])
lower_green = np.array([50, 50, 50])
upper_green = np.array([70, 255, 255])
```
步骤5:创建掩码
```
mask_red = cv2.inRange(hsv, lower_red, upper_red)
mask_green = cv2.inRange(hsv, lower_green, upper_green)
```
步骤6:应用形态学操作
```
kernel = np.ones((5,5),np.uint8)
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_OPEN, kernel)
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel)
mask_green = cv2.morphologyEx(mask_green, cv2.MORPH_OPEN, kernel)
mask_green = cv2.morphologyEx(mask_green, cv2.MORPH_CLOSE, kernel)
```
步骤7:计算轮廓
```
contours_red, _ = cv2.findContours(mask_red, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_green, _ = cv2.findContours(mask_green, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
```
步骤8:绘制轮廓
```
cv2.drawContours(img, contours_red, -1, (0, 0, 255), 3)
cv2.drawContours(img, contours_green, -1, (0, 255, 0), 3)
```
步骤9:显示结果
```
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
完整代码如下:
```
import cv2
import numpy as np
img = cv2.imread('image.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_red = np.array([0, 50, 50])
upper_red = np.array([10, 255, 255])
lower_green = np.array([50, 50, 50])
upper_green = np.array([70, 255, 255])
mask_red = cv2.inRange(hsv, lower_red, upper_red)
mask_green = cv2.inRange(hsv, lower_green, upper_green)
kernel = np.ones((5,5),np.uint8)
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_OPEN, kernel)
mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel)
mask_green = cv2.morphologyEx(mask_green, cv2.MORPH_OPEN, kernel)
mask_green = cv2.morphologyEx(mask_green, cv2.MORPH_CLOSE, kernel)
contours_red, _ = cv2.findContours(mask_red, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_green, _ = cv2.findContours(mask_green, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours_red, -1, (0, 0, 255), 3)
cv2.drawContours(img, contours_green, -1, (0, 255, 0), 3)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```