import numpy as np
import cv2
import tensorflow as tf
# 加载预训练的模型(仅用于演示,实际模型可能更复杂)
model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=True)
# 加载视频
video_path = 'path_to_your_video.mp4'
cap = cv2.VideoCapture(video_path)
# 初始化目标追踪器
tracker = cv2.TrackerCSRT_create()
# 读取第一帧
ret, frame = cap.read()
bbox = cv2.selectROI('Select Object', frame, False)
tracker.init(frame, bbox)
while True:
ret, frame = cap.read()
if not ret:
break
# 更新目标追踪器
ret, bbox = tracker.update(frame)
if ret:
# 提取目标图像区域
x, y, w, h = [int(i) for i in bbox]
target_image = frame[y:y+h, x:x+w]
target_image = cv2.resize(target_image, (224, 224))
target_image = tf.keras.applications.mobilenet_v2.preprocess_input(target_image)
target_image = np.expand_dims(target_image, axis=0)
# 使用模型进行行为预测
predictions = model.predict(target_image)
predicted_class = tf.keras.applications.mobilenet_v2.decode_predictions(predictions)[0][0][1]
# 在帧上绘制目标框和预测的行为
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2)
cv2.putText(frame, f'Predicted Behavior: {predicted_class}', (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Tracking', frame)
# 按下ESC键退出
if cv2.waitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()
机器视觉-基于目标追踪的智能运动分析与行为预测.zip
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2023-11-06
17:36:58
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