import cv2 import numpy as np from matplotlib import pyplot as plt from scipy.signal import find_peaks cap = cv2.VideoCapture(2) # 使用摄像头0,通常更稳定 cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) # 降低分辨率提高处理速度 cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) # 预先创建图形窗口,避免重复创建 fig, ax = plt.subplots(figsize=(10, 4)) plt.ion() ax.set_title('Saturation Channel Histogram') ax.set_xlabel('Saturation Value') ax.set_ylabel('Pixel Count') ax.set_xlim(0, 255) while True: ret, frame = cap.read() if not ret: print("Failed to grab frame") break cv2.imshow("Camera Feed", frame) # 直接提取饱和度通道,避免完整HSV转换 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) s = hsv[:, :, 0] s = s[s > 0] # 只保留非零饱和度值,减少噪声 # 使用更高效的直方图计算 hist = cv2.calcHist([s], [0], None, [256], [0, 256]) hist = hist.flatten() # 转换为一维数组 # 峰值检测 - 找到直方图中的峰值 peaks, properties = find_peaks(hist, height=np.max(hist) * 0.5, # 峰值高度至少是最大值的10% distance=10, # 峰值之间的最小距离 prominence=np.max(hist) * 0.2) # 峰值的突出度 # 清除旧数据并绘制新直方图 ax.clear() ax.plot(hist, 'b-', linewidth=1) # 标注峰值 if len(peaks) > 0: print(peaks) ax.text(0.5, 1.05, f'Found {len(peaks)} peaks') ax.plot(peaks, hist[peaks], 'ro', markersize=8, label=f'Peaks ({len(peaks)})') # 在峰值处添加文字标注 for i, peak in enumerate(peaks): ax.annotate(f'Peak {i+1}\n({peak}, {int(hist[peak])})', xy=(peak, hist[peak]), xytext=(peak, hist[peak] + np.max(hist) * 0.1), ha='center', va='bottom', bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7), arrowprops=dict(arrowstyle='->', color='red')) plt.draw() plt.pause(0.1) # 确保图形更新 key = cv2.waitKey(1) & 0xFF if key == ord('q'): break cap.release() cv2.destroyAllWindows() plt.close('all')