import cv2
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import dlib
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import os
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import numpy as np
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# 计算眼睛纵横比
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def eye_aspect_ratio(eye):
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A = np.linalg.norm(eye[1] - eye[5])
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B = np.linalg.norm(eye[2] - eye[4])
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C = np.linalg.norm(eye[0] - eye[3])
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ear = (A + B) / (2.0 * C)
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return ear
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# 检测头部运动（简单示例，可根据实际情况优化）
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def detect_head_movement(prev_face_landmarks, face_landmarks):
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if prev_face_landmarks is None:
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return False
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prev_nose = prev_face_landmarks[30]
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current_nose = face_landmarks[30]
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movement_threshold = 10 # 调整阈值
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return abs(current_nose[0] - prev_nose[0]) > movement_threshold or abs(
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current_nose[1] - prev_nose[1]
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) > movement_threshold
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# 直接指定路径和参数
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input_path = None # 如果需要从文件读取可以指定路径，这里设为None表示使用摄像头
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# 使用原始字符串来定义路径，避免转义问题
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output_path = r"H:\python_charm\python_project\database"
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detector_path = r"H:\python_charm\python_project\face_detector"
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confidence = 0.5
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# 加载人脸检测模型
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print("[INFO] loading face detector...")
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protoPath = os.path.sep.join([detector_path, "deploy.prototxt"])
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modelPath = os.path.sep.join([detector_path, "res10_300x300_ssd_iter_140000.caffemodel"])
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net = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
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# 创建输出目录
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if not os.path.exists(output_path):
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os.makedirs(output_path)
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real_output_dir = os.path.join(output_path, "real")
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fake_output_dir = os.path.join(output_path, "fake")
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if not os.path.exists(real_output_dir):
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os.makedirs(real_output_dir)
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if not os.path.exists(fake_output_dir):
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os.makedirs(fake_output_dir)
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# 初始化dlib的面部关键点检测器
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
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# 初始化参数
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EYE_AR_THRESH = 0.3
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EYE_AR_CONSEC_FRAMES = 3
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COUNTER = 0
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TOTAL = 0
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prev_face_landmarks = None
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has_shaken_head = False
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has_blinked = False
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saved_fake = 0
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saved_real = 0
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# 各自采集500张
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target_count = 500
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# 新增标志变量，用于记录是否正在连续采集
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is_continuous_capturing = False
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capture_type = None # 记录当前采集的类型（'real' 或 'fake'）
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# 打开摄像头
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cap = cv2.VideoCapture(0)
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print("[INFO] Automatically taking pictures...")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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(h, w) = frame.shape[:2]
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blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
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(300, 300), (104.0, 177.0, 123.0))
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net.setInput(blob)
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detections = net.forward()
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# 检测到的人脸
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if len(detections) > 0:
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i = np.argmax(detections[0, 0, :, 2])
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detection_confidence = detections[0, 0, i, 2] # 重命名变量，避免混淆
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if detection_confidence > confidence:
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box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
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(startX, startY, endX, endY) = box.astype("int")
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# 提取面部关键点
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rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
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face_landmarks = predictor(frame, rect)
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face_landmarks = np.array([[part.x, part.y] for part in face_landmarks.parts()])
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# 检测头部运动
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has_shaken_head = has_shaken_head or detect_head_movement(prev_face_landmarks, face_landmarks)
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prev_face_landmarks = face_landmarks
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# 检测眨眼动作
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left_eye = face_landmarks[42:48]
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right_eye = face_landmarks[36:42]
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left_ear = eye_aspect_ratio(left_eye)
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right_ear = eye_aspect_ratio(right_eye)
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ear = (left_ear + right_ear) / 2.0
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if ear < EYE_AR_THRESH:
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COUNTER += 1
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else:
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if COUNTER >= EYE_AR_CONSEC_FRAMES:
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TOTAL += 1
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has_blinked = has_blinked or True
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COUNTER = 0
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# 保存人脸区域
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face = frame[startY:endY, startX:endX]
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if is_continuous_capturing:
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if capture_type == "real":
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label = "real"
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saved = saved_real
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output_dir = real_output_dir
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else:
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label = "fake"
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saved = saved_fake
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output_dir = fake_output_dir
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p = os.path.sep.join([output_dir, f"{label}_{saved}.png"])
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cv2.imwrite(p, face)
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print(f"[INFO] saved {p} to disk")
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if label == "fake":
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saved_fake += 1
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else:
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saved_real += 1
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if (capture_type == "real" and saved_real >= target_count) or \
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(capture_type == "fake" and saved_fake >= target_count):
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is_continuous_capturing = False
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print(f"[INFO] Reached the target count for {capture_type} images.")
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else:
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print("[INFO] Please enter 'r' for real or 'f' for fake (q to quit): ")
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key = cv2.waitKey(0) & 0xFF
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if key == ord("r"):
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if saved_real < target_count:
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is_continuous_capturing = True
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capture_type = "real"
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else:
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print("[INFO] Already reached the target count for real images. Skipping.")
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elif key == ord("f"):
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if saved_fake < target_count:
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is_continuous_capturing = True
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capture_type = "fake"
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else:
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print("[INFO] Already reached the target count for fake images. Skipping.")
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elif key == ord("q"):
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break
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# 显示画面
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cv2.imshow("Frame", frame)
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key = cv2.waitKey(1) & 0xFF
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if key == ord("q"):
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break
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# 当真实和虚假图片都采集到500张时结束
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if saved_fake >= target_count and saved_real >= target_count:
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print("[INFO] Reached the target count for both real and fake images. Exiting...")
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break
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cap.release()
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cv2.destroyAllWindows()
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