diff --git a/src/Drone_gesture_operation/main.py b/src/Drone_gesture_operation/main.py index a13c6fcf8f..32083c3090 100644 --- a/src/Drone_gesture_operation/main.py +++ b/src/Drone_gesture_operation/main.py @@ -13,6 +13,11 @@ def __init__(self, target_fps=30): self.frame_interval = 1.0 / target_fps self.last_frame_time = time.time() + # 2. 肤色检测（适配明亮+暗光环境） + self.skin_lower_bright = np.array([0, 10, 50], np.uint8) + self.skin_upper_bright = np.array([30, 255, 255], np.uint8) + self.skin_lower_dark = np.array([0, 5, 15], np.uint8) + self.skin_upper_dark = np.array([40, 180, 200], np.uint8) # 2. 肤色检测（适配明亮+暗光环境，核心优化：新增暗光阈值） # 明亮环境阈值（保留原有，适配强光场景） self.skin_lower_bright = np.array([0, 10, 50], np.uint8) @@ -25,6 +30,21 @@ def __init__(self, target_fps=30): self.skin_upper = self.skin_upper_dark self.kernel = np.ones((5, 5), np.uint8) + # 3. 核心参数（细化两者特征差异，解决重叠问题） + # 握拳参数（收紧阈值，增加横向/方正特征约束） + self.fist_solidity = 0.85 # 从0.82升至0.85，收紧密实度，拉大与大拇指差距 + self.fist_area_ratio = 0.75 + # 手指计数参数 + self.defect_depth_threshold = 4 + self.min_contour_area = 300 + # 大拇指识别参数（强化纵向特征，与握拳形成明显差异） + self.thumb_aspect_ratio = 0.6 + self.thumb_solidity_range = (0.4, 0.82) # 上限设为0.82，与握拳阈值0.85无重叠 + self.thumb_defect_max = 3 + + # 4. 缓存参数 + self.gesture_buffer = [] + self.buffer_size = 3 # 3. 核心参数（精准适配手势特征，优化暗光下轮廓识别） # 2. 肤色检测（适配更多光线） self.skin_lower = np.array([0, 10, 50], np.uint8) @@ -161,6 +181,7 @@ def analyze_contour(self, cnt): c = np.linalg.norm(np.array(end) - np.array(far)) angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) * 180 / np.pi if (b * c) > 0 else 0 + if depth > self.defect_depth_threshold and angle < 100: if depth > self.defect_depth_threshold and angle < 90: valid_defects.append((depth, angle, far)) defect_count += 1 @@ -185,6 +206,28 @@ def analyze_contour(self, cnt): return None def is_fist(self, features): + """优化握拳（stop）判定：增加方正/横向轮廓排除，避免误判大拇指""" + if not features: + return False + # 握拳核心特征：高密实度 + 低缺陷数 + 方正/横向轮廓（h <= w，排除纵向大拇指） + return (features["solidity"] > self.fist_solidity and + features["defect_count"] <= 1 and + abs(features["aspect_ratio"] - 1) < 0.3 and + features["h"] <= features["w"]) # 新增：握拳高度不大于宽度，排除纵向大拇指 + + def is_thumb_up(self, features): + """强化竖大拇指（up）纵向特征，与握拳形成明显差异""" + if not features: + return False + # 大拇指核心特征：窄高轮廓 + 适中密实度 + 少量缺陷 + 强纵向延伸 + return (features["aspect_ratio"] < self.thumb_aspect_ratio and + self.thumb_solidity_range[0] < features["solidity"] < self.thumb_solidity_range[1] and + features["defect_count"] <= self.thumb_defect_max and + features["hull_aspect"] < 0.7 and + features["h"] > features["w"] * 1.2) # 强化纵向：高度大于宽度1.2倍，与握拳形成差距 + + def capture_frames(self, cap): + """帧采集线程""" """稳定识别握拳（stop）""" if not features: return False @@ -311,6 +354,7 @@ def capture_frames(self, cap): time.sleep(self.frame_interval * 0.5) def process_frame(self, frame): + """核心处理逻辑：调整手势判断优先级，先up后stop""" """核心处理逻辑（暗光增强优化）""" """核心处理逻辑""" frame = cv.flip(frame, 1) @@ -319,6 +363,28 @@ def process_frame(self, frame): current_gesture = "None" if roi.size > 0: + # 预处理（暗光增强+去噪） + roi_small = cv.resize(roi, (400, 300)) + alpha = 1.8 + beta = 40 + roi_enhanced = cv.convertScaleAbs(roi_small, alpha=alpha, beta=beta) + roi_denoised = cv.GaussianBlur(roi_enhanced, (5, 5), 0) + + # 自适应亮度判断 + gray_roi = cv.cvtColor(roi_small, cv.COLOR_BGR2GRAY) + avg_brightness = np.mean(gray_roi) + if avg_brightness < 50: + self.skin_lower = self.skin_lower_dark + self.skin_upper = self.skin_upper_dark + else: + self.skin_lower = self.skin_lower_bright + self.skin_upper = self.skin_upper_bright + + # 肤色掩码提取+形态学优化 + hsv = cv.cvtColor(roi_denoised, cv.COLOR_BGR2HSV) + mask = cv.inRange(hsv, self.skin_lower, self.skin_upper) + mask = cv.morphologyEx(mask, cv.MORPH_OPEN, self.kernel, iterations=1) + mask = cv.morphologyEx(mask, cv.MORPH_DILATE, self.kernel, iterations=2) # 预处理（暗光增强：亮度+对比度+去噪+形态学，核心优化） roi_small = cv.resize(roi, (400, 300)) @@ -363,6 +429,39 @@ def process_frame(self, frame): features = self.analyze_contour(cnt) if features and features["area"] > self.min_contour_area: + # 手动轮廓坐标缩放+偏移 + scale_x = roi.shape[1] / float(roi_small.shape[1]) + scale_y = roi.shape[0] / float(roi_small.shape[0]) + cnt_scaled = cnt.astype(np.float64) + cnt_scaled[:, :, 0] *= scale_x + cnt_scaled[:, :, 1] *= scale_y + cnt_scaled[:, :, 0] += roi_x + cnt_scaled[:, :, 1] += roi_y + cnt_scaled = cnt_scaled.astype(np.int32) + cnt_scaled[:, :, 0] = np.clip(cnt_scaled[:, :, 0], 0, frame.shape[1]-1) + cnt_scaled[:, :, 1] = np.clip(cnt_scaled[:, :, 1], 0, frame.shape[0]-1) + + cv.drawContours(frame, [cnt_scaled], -1, (255, 0, 0), 2) + + # ========== 核心优化：调整手势判断优先级（先up后stop） ========== + # 1. 优先判断竖大拇指（up）- 避免被stop提前拦截 + if self.is_thumb_up(features): + current_gesture = "up" + # 2. 再判断握拳（stop）- 此时已排除大拇指，无误判 + elif self.is_fist(features): + current_gesture = "stop" + # 3. 其他手势判断 + elif features["defect_count"] == 1: + current_gesture = "front" + elif features["defect_count"] >= 3: + current_gesture = "back" + else: + current_gesture = "None" + + # 缓存稳定性增强 + self.gesture_buffer.append(current_gesture) + if len(self.gesture_buffer) > self.buffer_size: + self.gesture_buffer.pop(0) # 绘制轮廓（调试用，可直观看到手部提取效果） # 绘制轮廓（调试用） self.frame_queue = [frame] # 只保留最新帧 @@ -662,6 +761,10 @@ def main(): return frame_show def run(self): + """主运行逻辑""" + # 摄像头初始化 + cap = cv.VideoCapture(0) + cap.set(cv.CAP_PROP_FRAME_WIDTH, 640) """主运行逻辑（修复时间计算错误）""" # 摄像头初始化 cap = cv.VideoCapture(0) @@ -682,6 +785,16 @@ def run(self): # 提示信息 print("=" * 60) print(f"✅ 帧率锁定 {self.target_fps} 帧 | ESC退出") + print("💡 修复up误判为stop（优先级+特征优化）：") + print(" 👍 竖大拇指 → up（优先判断，精准识别）") + print(" ✊ 握拳 → stop（排除大拇指，无重叠）") + print(" 🤘 食指+中指 → front") + print(" 🖐️ 手掌张开 → back") + print("📌 已解决up与stop的特征重叠问题") + print("=" * 60) + + # 主循环 + while cap.isOpened(): print("💡 暗光优化版手势识别（高稳定性）：") print("💡 优化版手势识别（高稳定性）：") print(" ✊ 握拳 → stop（高稳定）") @@ -762,6 +875,9 @@ def run(self): # 处理并显示 frame_show = self.process_frame(frame) + cv.imshow("Hand Gesture Recognition (Fix UP→Stop Misjudgment)", frame_show) + + # 更新时间戳 cv.imshow("Hand Gesture Recognition (Dark Mode Optimized)", frame_show) # 更新时间戳 @@ -788,6 +904,7 @@ def run(self): if __name__ == '__main__': recognizer = StableFPSHandRecognizer(target_fps=20) recognizer.run() + recognizer.run() # 20帧兼顾流畅度和识别稳定性 recognizer = StableFPSHandRecognizer(target_fps=20) recognizer.run()