30秒轻松实现TensorFlow物体检测

原创
ithorizon 1个月前 (10-03) 阅读数 196 #Python

30秒迅捷实现TensorFlow物体检测

30秒轻松实现TensorFlow物体检测

在人工智能领域,物体检测是一个非常热门的话题。TensorFlow作为一款优秀的开源机器学习框架,为我们提供了明了易用的物体检测API。接下来,我们将通过一个明了的例子,展示怎样在30秒内实现TensorFlow物体检测。

准备工作

首先,确保你已经安装了TensorFlow和TensorFlow Object Detection API。如果还没有安装,可以参考官方文档进行安装。

编写代码

下面是一段使用TensorFlow Object Detection API进行物体检测的示例代码:

import cv2

import numpy as np

import tensorflow as tf

# 加载模型

model_path = 'path/to/your/model'

detection_graph = tf.Graph()

with detection_graph.as_default():

od_graph_def = tf.GraphDef()

with tf.gfile.GFile(model_path, 'rb') as fid:

serialized_graph = fid.read()

od_graph_def.ParseFromString(serialized_graph)

tf.import_graph_def(od_graph_def, name='')

# 定义标签列表

labels = ['person', 'car', 'bus', 'truck']

# 初始化OpenCV窗口

cv2.namedWindow('object_detection', cv2.WINDOW_NORMAL)

# 处理视频流或图片

cap = cv2.VideoCapture('path/to/your/video.mp4')

with detection_graph.as_default():

with tf.Session(graph=detection_graph) as sess:

while True:

ret, image_np = cap.read()

if not ret:

break

# 获取图像的形状

image_np_expanded = np.expand_dims(image_np, axis=0)

# 获取模型输出

image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')

detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# 运行模型

(boxes, scores, classes, num) = sess.run(

[detection_boxes, detection_scores, detection_classes, num_detections],

feed_dict={image_tensor: image_np_expanded})

# 可视化最终

for i in range(int(num[0])):

if scores[0][i] > 0.5:

box = boxes[0][i]

ymin = box[0] * image_np.shape[0]

xmin = box[1] * image_np.shape[1]

ymax = box[2] * image_np.shape[0]

xmax = box[3] * image_np.shape[1]

cv2.rectangle(image_np, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 0, 255), 2)

cv2.putText(image_np, labels[int(classes[0][i]) - 1], (int(xmin), int(ymin - 5)),

cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)

cv2.imshow('object_detection', image_np)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

cv2.destroyAllWindows()

总结

通过以上示例,我们仅用30秒就实现了TensorFlow物体检测功能。需要注意的是,这个例子仅展示了怎样使用预训练模型进行物体检测。要实现更精确的物体检测,还需要进行数据准备、模型训练等步骤。


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文章标签: Python


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