实验2-颜色识别检测
pip install opencv-python #下载open-cv数据库（另外需自行安装python3，如已下载可忽略）cd OPENCV#打开OPENCV功能包sudo python3 ./color_detection.py#运行py文件
终端显示：
此时Linux系统上会显示摄像头实时画面，我们需要在窗口焦点下测试按键，效果如下：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
多颜色同时识别程序
功能：实时识别摄像头中的多种颜色物体
"""
import cv2
import numpy as np
import sys
import os
import argparse
def main():
"""
主函数：打开摄像头并进行多颜色同时识别
"""
# 解析命令行参数
parser = argparse.ArgumentParser(description='多颜色同时识别程序')
parser.add_argument('--width', type=int, default=2560, help='显示窗口宽度')
parser.add_argument('--height', type=int, default=1440, help='显示窗口高度')
args = parser.parse_args()
# 打开默认摄像头
cap = cv2.VideoCapture(0)
# 检查摄像头是否成功打开
if not cap.isOpened():
print("错误：无法打开摄像头")
sys.exit(1)
# 设置摄像头分辨率
cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.height)
# 创建窗口并设置大小
cv2.namedWindow('Original', cv2.WINDOW_NORMAL)
cv2.namedWindow('Color Detection', cv2.WINDOW_NORMAL)
cv2.namedWindow('Controls', cv2.WINDOW_NORMAL)
# 设置窗口大小
cv2.resizeWindow('Original', args.width // 2, args.height // 2)
cv2.resizeWindow('Color Detection', args.width // 2, args.height // 2)
cv2.resizeWindow('Controls', 600, 300)
# 创建HSV颜色范围的滑动条
cv2.createTrackbar('H_min', 'Controls', 0, 179, lambda x: None)
cv2.createTrackbar('H_max', 'Controls', 179, 179, lambda x: None)
cv2.createTrackbar('S_min', 'Controls', 0, 255, lambda x: None)
cv2.createTrackbar('S_max', 'Controls', 255, 255, lambda x: None)
cv2.createTrackbar('V_min', 'Controls', 0, 255, lambda x: None)
cv2.createTrackbar('V_max', 'Controls', 255, 255, lambda x: None)
# 定义颜色范围和对应的颜色名称及显示颜色
color_ranges = {
'red': {
'ranges': [(0, 50, 50), (10, 255, 255), (160, 50, 50), (179, 255, 255)], # 红色有两个范围
'color': (0, 0, 255) # BGR格式：蓝=0, 绿=0, 红=255
},
'green': {
'ranges': [(35, 50, 50), (85, 255, 255)],
'color': (0, 255, 0) # BGR格式：蓝=0, 绿=255, 红=0
},
'blue': {
'ranges': [(100, 50, 50), (130, 255, 255)],
'color': (255, 0, 0) # BGR格式：蓝=255, 绿=0, 红=0
},
'yellow': {
'ranges': [(20, 100, 100), (30, 255, 255)],
'color': (0, 255, 255) # BGR格式：蓝=0, 绿=255, 红=255
},
'white': {
'ranges': [(0, 0, 200), (180, 30, 255)],
'color': (255, 255, 255) # BGR格式：蓝=255, 绿=255, 红=255
},
'black': {
'ranges': [(0, 0, 0), (180, 255, 30)],
'color': (0, 0, 0) # BGR格式：蓝=0, 绿=0, 红=0
}
}
# 设置初始滑动条位置为自定义颜色
cv2.setTrackbarPos('H_min', 'Controls', 0)
cv2.setTrackbarPos('S_min', 'Controls', 0)
cv2.setTrackbarPos('V_min', 'Controls', 0)
cv2.setTrackbarPos('H_max', 'Controls', 179)
cv2.setTrackbarPos('S_max', 'Controls', 255)
cv2.setTrackbarPos('V_max', 'Controls', 255)
print("多颜色同时识别程序已启动")
print("按键说明：")
print("- 'q'：退出程序")
print("- 's'：保存当前帧和检测结果")
print("- '+'/'-'：调整窗口大小")
# 循环读取摄像头画面
while True:
# 读取一帧图像
ret, frame = cap.read()
# 如果读取失败，退出循环
if not ret:
print("错误：无法读取摄像头画面")
break
# 转换到HSV颜色空间
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# 获取当前滑动条的值（用于自定义颜色检测）
h_min = cv2.getTrackbarPos('H_min', 'Controls')
h_max = cv2.getTrackbarPos('H_max', 'Controls')
s_min = cv2.getTrackbarPos('S_min', 'Controls')
s_max = cv2.getTrackbarPos('S_max', 'Controls')
v_min = cv2.getTrackbarPos('V_min', 'Controls')
v_max = cv2.getTrackbarPos('V_max', 'Controls')
# 创建自定义颜色掩码
custom_lower = np.array([h_min, s_min, v_min])
custom_upper = np.array([h_max, s_max, v_max])
custom_mask = cv2.inRange(hsv, custom_lower, custom_upper)
# 创建检测结果图像
detection_frame = frame.copy()
# 处理自定义颜色
contours, _ = cv2.findContours(custom_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
area = cv2.contourArea(contour)
if area < 500: # 忽略太小的轮廓
continue
# 绘制轮廓
cv2.drawContours(detection_frame, [contour], -1, (255, 255, 0), 2) # 青色
# 计算轮廓的外接矩形
x, y, w, h = cv2.boundingRect(contour)
# 在矩形上方显示"自定义"
cv2.putText(detection_frame, "Custom", (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
# 绘制矩形框
cv2.rectangle(detection_frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
# 对每种预定义颜色进行检测
for color_name, color_info in color_ranges.items():
# 创建掩码
if color_name == 'red': # 红色需要特殊处理（两个范围）
lower1 = np.array(color_info['ranges'][0])
upper1 = np.array(color_info['ranges'][1])
lower2 = np.array(color_info['ranges'][2])
upper2 = np.array(color_info['ranges'][3])
mask1 = cv2.inRange(hsv, lower1, upper1)
mask2 = cv2.inRange(hsv, lower2, upper2)
color_mask = cv2.bitwise_or(mask1, mask2)
else:
lower = np.array(color_info['ranges'][0])
upper = np.array(color_info['ranges'][1])
color_mask = cv2.inRange(hsv, lower, upper)
# 查找轮廓
contours, _ = cv2.findContours(color_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 处理轮廓
for contour in contours:
area = cv2.contourArea(contour)
if area < 500: # 忽略太小的轮廓
continue
# 绘制轮廓
cv2.drawContours(detection_frame, [contour], -1, color_info['color'], 2)
# 计算轮廓的外接矩形
x, y, w, h = cv2.boundingRect(contour)
# 在矩形上方显示颜色名称
cv2.putText(detection_frame, color_name, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, color_info['color'], 2)
# 绘制矩形框
cv2.rectangle(detection_frame, (x, y), (x + w, y + h), color_info['color'], 2)
# 显示图像
cv2.imshow('Original', frame)
cv2.imshow('Color Detection', detection_frame)
# 等待按键
key = cv2.waitKey(30) & 0xFF
# 处理按键
if key == ord('q'):
print("用户退出程序")
break
elif key == ord('s'):
# 创建保存目录
save_dir = "color_detection_images"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 生成文件名
import time
timestamp = time.strftime("%Y%m%d_%H%M%S")
original_filename = os.path.join(save_dir, f"original_{timestamp}.jpg")
detection_filename = os.path.join(save_dir, f"detection_{timestamp}.jpg")
# 保存图像
cv2.imwrite(original_filename, frame)
cv2.imwrite(detection_filename, detection_frame)
print(f"已保存图像: {original_filename}, {detection_filename}")
elif key == ord('+') or key == ord('='): # '='键和'+'键通常在同一个键位
# 增大窗口
current_width = cv2.getWindowImageRect('Color Detection')[2]
current_height = cv2.getWindowImageRect('Color Detection')[3]
new_width = int(current_width * 1.1)
new_height = int(current_height * 1.1)
cv2.resizeWindow('Original', new_width, new_height)
cv2.resizeWindow('Color Detection', new_width, new_height)
print(f"窗口大小增加到: {new_width}x{new_height}")
elif key == ord('-'):
# 减小窗口
current_width = cv2.getWindowImageRect('Color Detection')[2]
current_height = cv2.getWindowImageRect('Color Detection')[3]
new_width = int(current_width * 0.9)
new_height = int(current_height * 0.9)
cv2.resizeWindow('Original', new_width, new_height)
cv2.resizeWindow('Color Detection', new_width, new_height)
print(f"窗口大小减小到: {new_width}x{new_height}")
# 释放资源
cap.release()
cv2.destroyAllWindows()
print("程序已退出")
if __name__ == "__main__":
try:
main()
except Exception as e:
print(f"程序发生错误: {e}")
sys.exit(1)