09 | TensorFlow物体检测

简介

TensorFlow提供了用于检测图片或视频中所包含物体的API,详情可参考以下链接

github.com/tensorflow/…

物体检测和图片分类不同

  • 图片分类是将图片分为某一类别,即从多个可能的分类中选择一个,即使可以按照概率输出最可能的多个分类,但理论上的正确答案只有一个
  • 物体检测是检测图片中所出现的全部物体并且用矩形(Anchor Box)进行标注,物体的类别可以包括多种,例如人、车、动物、路标等,即正确答案可以是多个

通过多个例子,了解TensorFlow物体检测API的使用方法

这里使用预训练好的ssd_mobilenet_v1_coco模型(Single Shot MultiBox Detector),更多可用的物体检测模型可以参考这里

github.com/tensorflow/…

举个例子

加载库

# -*- coding: utf-8 -*-

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image

from utils import label_map_util
from utils import visualization_utils as vis_util

定义一些常量

PATH_TO_CKPT = 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb'
PATH_TO_LABELS = 'ssd_mobilenet_v1_coco_2017_11_17/mscoco_label_map.pbtxt'
NUM_CLASSES = 90

加载预训练好的模型

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        od_graph_def.ParseFromString(fid.read())
        tf.import_graph_def(od_graph_def, name='')

加载分类标签数据

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

一个将图片转为数组的辅助函数,以及测试图片路径

def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)

TEST_IMAGE_PATHS = ['test_images/image1.jpg', 'test_images/image2.jpg']

使用模型进行物体检测

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        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')
        for image_path in TEST_IMAGE_PATHS:
            image = Image.open(image_path)
            image_np = load_image_into_numpy_array(image)
            image_np_expanded = np.expand_dims(image_np, axis=0)
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections], 
                feed_dict={image_tensor: image_np_expanded})

            vis_util.visualize_boxes_and_labels_on_image_array(image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8)
            plt.figure(figsize=[12, 8])
            plt.imshow(image_np)
            plt.show()

检测结果如下,第一张图片检测出了两只狗狗

第二张图片检测出了一些人和风筝

摄像头检测

安装OpenCV,用于实现和计算机视觉相关的功能,版本为3.3.0.10

pip install opencv-python opencv-contrib-python -i https://pypi.tuna.tsinghua.edu.cn/simple

查看是否安装成功,没有报错即可

import cv2
tracker = cv2.TrackerMedianFlow_create()

在以上代码的基础上进行修改

  • 加载cv2并获取摄像头
  • 不断地从摄像头获取图片
  • 将检测后的结果输出

完整代码如下

# -*- coding: utf-8 -*-

import numpy as np
import tensorflow as tf

from utils import label_map_util
from utils import visualization_utils as vis_util

import cv2
cap = cv2.VideoCapture(0)

PATH_TO_CKPT = 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb'
PATH_TO_LABELS = 'ssd_mobilenet_v1_coco_2017_11_17/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:
        od_graph_def.ParseFromString(fid.read())
        tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        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')
        while True:
            ret, image_np = cap.read()
            image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
            image_np_expanded = np.expand_dims(image_np, axis=0)
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections], 
                feed_dict={image_tensor: image_np_expanded})

            vis_util.visualize_boxes_and_labels_on_image_array(image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8)

            cv2.imshow('object detection', cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR))
            if cv2.waitKey(25) & 0xFF == ord('q'):
                cap.release()
                cv2.destroyAllWindows()
                break

视频检测

使用cv2读取视频并获取每一帧图片,然后将检测后的每一帧写入新的视频文件

生成的视频文件只有图像、没有声音,关于音频的处理以及视频和音频的合成,后面再进一步探索

完整代码如下

# -*- coding: utf-8 -*-

import numpy as np
import tensorflow as tf

from utils import label_map_util
from utils import visualization_utils as vis_util

import cv2
cap = cv2.VideoCapture('绝地逃亡.mov')
ret, image_np = cap.read()
out = cv2.VideoWriter('output.mov', -1, cap.get(cv2.CAP_PROP_FPS), (image_np.shape[1], image_np.shape[0]))

PATH_TO_CKPT = 'ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb'
PATH_TO_LABELS = 'ssd_mobilenet_v1_coco_2017_11_17/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:
        od_graph_def.ParseFromString(fid.read())
        tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        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')
        while cap.isOpened():
            ret, image_np = cap.read()
            if len((np.array(image_np)).shape) == 0:
                break

            image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
            image_np_expanded = np.expand_dims(image_np, axis=0)

            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections], 
                feed_dict={image_tensor: image_np_expanded})

            vis_util.visualize_boxes_and_labels_on_image_array(image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8)
            out.write(cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR))

cap.release()
out.release()
cv2.destroyAllWindows()

播放处理好的视频,可以看到很多地方都有相应的检测结果

参考

Introduction and Use - Tensorflow Object Detection API Tutorial:pythonprogramming.net/introductio…

Tensorflow Object Detection API:github.com/tensorflow/…

SSD - Single Shot MultiBox Detector:arxiv.org/pdf/1512.02…

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