python实时视频目标检测
时间: 2024-06-02 07:06:50 浏览: 18
Python实时视频目标检测可以通过使用深度学习框架中的一些预训练模型来实现。其中比较常用的模型有YOLO和SSD。这些模型通过对输入视频流的每一帧进行处理,可以识别出其中包含的物体,并在视频流中实时标注出物体的位置和类别。在Python中,可以使用OpenCV库对视频流进行读取和处理,然后将每一帧送入目标检测模型中进行处理。
具体步骤如下:
1. 加载目标检测模型(如YOLO或SSD);
2. 使用OpenCV库读取视频流;
3. 对每一帧图像进行处理,包括图像预处理和后处理;
4. 将处理后的图像送入目标检测模型进行识别;
5. 标注出检测到的物体的位置和类别,并在视频流中显示。
相关问题
车辆视频目标检测 python代码
以下是使用Python和OpenCV进行车辆视频目标检测的代码示例:
```python
import cv2
# 加载Haar级联分类器
car_cascade = cv2.CascadeClassifier('path/to/haarcascade_car.xml')
# 读取输入视频
cap = cv2.VideoCapture('path/to/input/video.mp4')
# 检测车辆并标记
while True:
# 从视频流中读取一帧
ret, frame = cap.read()
# 如果视频结束,跳出循环
if not ret:
break
# 转换为灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测车辆
cars = car_cascade.detectMultiScale(gray, 1.1, 4)
# 标记检测到的车辆
for (x, y, w, h) in cars:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
# 显示标记后的帧
cv2.imshow('Car Detection', frame)
# 按下q键退出程序
if cv2.waitKey(25) & 0xFF == ord('q'):
break
# 释放视频流和窗口
cap.release()
cv2.destroyAllWindows()
```
在上面的代码中,我们首先加载Haar级联分类器,然后使用OpenCV的VideoCapture函数读取输入视频。接下来,我们在循环中读取每一帧视频,并将其转换为灰度图像。然后,我们使用级联分类器检测车辆,使用cv2.rectangle函数在每个检测到的车辆周围绘制矩形,并在窗口中显示标记后的帧。最后,我们按下q键退出程序,并释放视频流和窗口。
生成一个python的视频目标检测代码
好的,以下是一个使用 TensorFlow Object Detection API 进行目标检测的 Python 代码:
```python
import os
import cv2
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# 加载模型
MODEL_NAME = 'ssd_inception_v2_coco_2018_01_28'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# 加载标签
label_map = {}
with open(PATH_TO_LABELS, 'r') as f:
for line in f.readlines():
if 'id:' in line:
idx = int(line.split(':')[1])
elif 'name:' in line:
name = line.split(':')[1].strip()
label_map[idx] = name
# 执行目标检测
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
cap = cv2.VideoCapture('input.mp4') # 输入视频文件
writer = None
while True:
ret, image_np = cap.read()
if not ret:
break
# 执行目标检测
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# 绘制检测框
boxes = np.squeeze(boxes)
scores = np.squeeze(scores)
classes = np.squeeze(classes).astype(np.int32)
for i in range(num_detections):
if scores[i] > 0.5:
ymin, xmin, ymax, xmax = tuple(boxes[i])
im_height, im_width, _ = image_np.shape
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
cv2.rectangle(image_np, (int(left), int(top)), (int(right), int(bottom)), (0, 255, 0), 2)
cv2.putText(image_np, label_map[classes[i]], (int(left), int(top)-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 写入输出视频文件
if writer is None:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter("output.avi", fourcc, 30, (image_np.shape[1], image_np.shape[0]), True)
writer.write(image_np)
cap.release()
writer.release()
```
注意,此代码需要使用 TensorFlow 1.x 版本和 Object Detection API。此外,需要提前下载 SSD Inception V2 COCO 模型和 MSCOCO 标签文件。在运行代码之前,需要将输入视频文件路径和输出视频文件路径替换为实际的路径。