import cv2 import numpy as np import motor import time import signal import sys # 信号处理函数，用于捕获Ctrl+C def signal_handler(sig, frame): print("\n接收到中断信号，正在停止电机...") motor.stop() print("电机已停止，程序退出") sys.exit(0) # 注册信号处理函数 signal.signal(signal.SIGINT, signal_handler) print("开始运行摄像头程序...") print(f"OpenCV版本: {cv2.__version__}") # 尝试打开摄像头 cap = cv2.VideoCapture(0) if not cap.isOpened(): print("无法打开摄像头！") exit() print("摄像头打开成功") # 设置摄像头参数 cap.set(3, 320) cap.set(4, 240) cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) print(f"摄像头分辨率: {cap.get(3)}x{cap.get(4)}") # 初始化稳定阶段 print("初始化摄像头...") success_count = 0 for i in range(10): ret, frame = cap.read() if not ret: print(f"第{i+1}帧读取失败") continue success_count += 1 print(f"第{i+1}帧读取成功") time.sleep(0.1) print(f"摄像头初始化完成，成功读取{success_count}帧") # 读取初始帧 ret, prev_frame = cap.read() if not ret: print("无法读取初始帧！") cap.release() exit() print("初始帧读取成功") prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) prev_gray = cv2.GaussianBlur(prev_gray, (5,5), 0) print("初始帧处理完成") # 记录上一帧目标的位置和面积 prev_cx = None prev_cy = None prev_area = None # 目标跟踪状态 tracking_target = None while True: ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5,5), 0) # 帧差 diff = cv2.absdiff(prev_gray, gray) diff = cv2.convertScaleAbs(diff, alpha=2.5) # 应用形态学操作，去除噪声 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) diff = cv2.morphologyEx(diff, cv2.MORPH_OPEN, kernel) diff = cv2.morphologyEx(diff, cv2.MORPH_CLOSE, kernel) _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY) contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # ===== 核心：目标检测和跟踪 ===== target = None max_area = 0 # 找到所有符合条件的轮廓 valid_contours = [] for cnt in contours: area = cv2.contourArea(cnt) if area < 75: # 过滤噪声，增大阈值 continue if area > 5000: # 过滤大面积假目标（如整个画面），减小阈值 continue # 计算轮廓的宽高比，过滤不符合垃圾特征的目标 x, y, w, h = cv2.boundingRect(cnt) aspect_ratio = float(w) / h if h > 0 else 0 if aspect_ratio > 3 or aspect_ratio < 0.3: # 过滤过于狭长的目标 continue valid_contours.append((area, cnt, x, y, w, h)) # 如果有有效的轮廓 if valid_contours: # 如果正在跟踪目标，优先选择与上一目标位置接近的轮廓 if tracking_target is not None and prev_cx is not None and prev_cy is not None: best_match = None min_distance = float('inf') search_radius = 80 # 目标锁定区域半径 for area, cnt, x, y, w, h in valid_contours: cx = x + w // 2 cy = y + h // 2 # 计算与上一目标的距离 distance = ((cx - prev_cx) ** 2 + (cy - prev_cy) ** 2) ** 0.5 # 只考虑在锁定区域内的目标 if distance > search_radius: continue # 如果距离小于阈值，认为是同一个目标 if distance < 50: # 距离阈值 if distance < min_distance: min_distance = distance best_match = (area, cnt, x, y, w, h) # 如果找到匹配的目标 if best_match: area, cnt, x, y, w, h = best_match target = cnt max_area = area else: # 如果没有找到匹配的目标，选择最大的轮廓 valid_contours.sort(key=lambda x: x[0], reverse=True) area, cnt, x, y, w, h = valid_contours[0] target = cnt max_area = area # 开始跟踪新目标 tracking_target = True else: # 如果没有正在跟踪的目标，选择最大的轮廓 valid_contours.sort(key=lambda x: x[0], reverse=True) area, cnt, x, y, w, h = valid_contours[0] target = cnt max_area = area # 开始跟踪新目标 tracking_target = True # ===== 只处理一个目标 ===== if target is not None: x, y, w, h = cv2.boundingRect(target) cx = x + w // 2 cy = y + h // 2 # 目标位置平滑处理 alpha = 0.6 if prev_cx is not None and prev_cy is not None: cx = int(alpha * prev_cx + (1 - alpha) * cx) cy = int(alpha * prev_cy + (1 - alpha) * cy) # 绘制画面中心 center_x = 90 # 画面中心x坐标 center_y = 120 # 画面中心y坐标 cv2.circle(frame, (center_x, center_y), 5, (255, 0, 0), -1) # 绘制目标矩形和中心 cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1) print("唯一目标:", cx, cy, "area:", max_area) # 检测目标是否突然跳变 target_jumped = False if prev_cx is not None and prev_cy is not None and prev_area is not None: # 计算位置变化 dx = abs(cx - prev_cx) dy = abs(cy - prev_cy) # 计算面积变化比例 area_ratio = max_area / prev_area if prev_area > 0 else 0 # 如果位置变化过大或面积变化过大，认为目标跳变 if dx > 100 or dy > 100 or area_ratio < 0.3 or area_ratio > 3: target_jumped = True print("目标突然跳变，可能已离开视野范围") # 重置跟踪状态 tracking_target = None # 控制逻辑：根据x轴和y轴坐标控制垃圾桶移动 center_x = 90 # 画面中心x坐标 center_y = 120 # 画面中心y坐标 threshold = 15 # 阈值范围 if target_jumped: # 目标跳变，停止移动 print("目标跳变，停止移动") motor.stop() else: # 计算目标与中心的偏差 dx = cx - center_x dy = cy - center_y # 确定移动方向 if abs(dx) < threshold and abs(dy) < threshold: # 目标在中心附近，停止移动 print("目标在中心，停止移动") motor.stop() elif abs(dx) < threshold: # 只在y轴方向有偏差 if dy > threshold: # 目标在下侧，垃圾桶向右移动 print("目标在下侧，向右移动") motor.move_right(speed=1.0) else: # 目标在上侧，垃圾桶向左移动 print("目标在上侧，向左移动") motor.move_left(speed=1.0) elif abs(dy) < threshold: # 只在x轴方向有偏差 if dx > threshold: # 目标在右侧，垃圾桶向后移动 print("目标在右侧，向后移动") motor.backward(speed=1.0) else: # 目标在左侧，垃圾桶向前移动 print("目标在左侧，向前移动") motor.forward(speed=1.0) else: # 在x轴和y轴方向都有偏差，使用斜向移动 if dx > threshold and dy > threshold: # 目标在右下侧，垃圾桶向右后移动 print("目标在右下侧，向右后移动") motor.move_right_backward(speed=1.0) elif dx > threshold and dy < -threshold: # 目标在右上侧，垃圾桶向左后移动 print("目标在右上侧，向左后移动") motor.move_left_backward(speed=1.0) elif dx < -threshold and dy > threshold: # 目标在左下侧，垃圾桶向右前移动 print("目标在左下侧，向右前移动") motor.move_right_forward(speed=1.0) else: # 目标在左上侧，垃圾桶向左前移动 print("目标在左上侧，向左前移动") motor.move_left_forward(speed=1.0) # 更新上一帧的目标信息 prev_cx = cx prev_cy = cy prev_area = max_area else: # 没有检测到目标，停止移动 print("未检测到目标，停止移动") motor.stop() # 重置上一帧的目标信息 prev_cx = None prev_cy = None prev_area = None # 重置跟踪状态 tracking_target = None cv2.imshow("tracking", frame) prev_gray = gray.copy() if cv2.waitKey(1) & 0xFF == 27: break cap.release() cv2.destroyAllWindows() motor.stop()