Object Detection物体检测（图像、视频、摄像头）

左手的ㄟ右手 2023-06-02 09:13 20阅读 0赞

> [预训练模型下载地址][Link 1]

> ### 一、对图像 ###
> 
>     import numpy as np
>     import tensorflow as tf
>     from PIL import Image
>     from tensorflow.models.research.object_detection.utils import label_map_util
>     from tensorflow.models.research.object_detection.utils import visualization_utils as vis_util
>     '''
>     1. 导入模型
>     2. 导入标签图像
>     3. 图像转换分析
>     4. 打印图像
>     '''
>     
>     PB_PATH = 'ssd_inception_v2_coco_2018_01_28/frozen_inference_graph.pb'
>     LABEL_PATH = 'data/mscoco_label_map.pbtxt'
>     
>     od_graph = tf.Graph()
>     with od_graph.as_default():
>         od_graph_def = tf.GraphDef()
>         # 打开文件，rb是读取二进制，同open()
>         with tf.gfile.FastGFile(PB_PATH, 'rb') as f:
>             od_graph_def.ParseFromString(f.read())
>             tf.import_graph_def(od_graph_def, name="")
>     
>     category_index = label_map_util.create_category_index_from_labelmap(LABEL_PATH)
>     
>     test_images_path = ["test_images/image{0}.jpg".format(i) for i in range(1, 4)]
>     
>     def image_to_np(image):
>         (im_w, im_h) = image.size
>         return np.array(image.getdata()).reshape(im_h, im_w, 3).astype(np.uint8)
>     
>     def run_inference_for_single_image(image):
>         with od_graph.as_default():
>             with tf.Session() as sess:
>                 tensor_dict = {}
>                 tensor_dict['num_detections'] = tf.get_default_graph().get_tensor_by_name('num_detections:0')
>                 tensor_dict['detection_boxes'] = tf.get_default_graph().get_tensor_by_name('detection_boxes:0')
>                 tensor_dict['detection_scores'] = tf.get_default_graph().get_tensor_by_name('detection_scores:0')
>                 tensor_dict['detection_classes'] = tf.get_default_graph().get_tensor_by_name('detection_classes:0')
>                 image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
>     
>                 output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image})
>     
>                 output_dict['num_detections'] = int(output_dict['num_detections'][0])
>                 output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
>                 output_dict['detection_scores'] = output_dict['detection_scores'][0]
>                 output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.int64)
>                 return output_dict
>     
>     for image_path in test_images_path:
>         # 打开图片 转成np格式，并扩展一维
>         image = Image.open(image_path)
>         image_np = image_to_np(image)
>         image_np_dim = np.expand_dims(image_np, axis=0)
>         output_dict = run_inference_for_single_image(image_np_dim)
>     
>         vis_util.visualize_boxes_and_labels_on_image_array(
>             image_np,
>             output_dict['detection_boxes'],
>             output_dict['detection_classes'],
>             output_dict['detection_scores'],
>             category_index,
>             use_normalized_coordinates=True)
>         img = Image.fromarray(image_np, 'RGB')
>         img.show()

> ### 二、对视频 ###
> 
>     import os
>     import time
>     import argparse
>     import multiprocessing
>     import numpy as np
>     import tensorflow as tf
>     import tarfile
>     from matplotlib import pyplot as plt
>     
>     from object_detection.utils import label_map_util
>     
>     from object_detection.utils import visualization_utils as vis_util
>     '''
>         视频目标追踪
>     '''
>     #1.得到模型 （这里首先下载流模型并在解压在path/to/models/research/object_detection里面）
>     MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
>     PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb')
>     
>     PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
>     
>     print('Loading model...')
>     
>     
>     #load frozen of tensorflow to memeory
>     detection_graph = tf.Graph()
>     with detection_graph.as_default():
>         od_graph_def = tf.GraphDef()
>         with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: #文本操作句柄，类似python里面的open()
>             serialized_graph = fid.read()
>             od_graph_def.ParseFromString(serialized_graph)
>             tf.import_graph_def(od_graph_def, name='')  #将图像从od_graph_def导入当前的默认Graph
>     
>     #label map to class name 如预测为5,知道它是对应飞机
>     NUM_CLASS = 90
>     
>     print("Loading label map...")
>     label_map = label_map_util.load_labelmap(PATH_TO_LABELS)  #得到label map proto
>     categories = label_map_util.convert_label_map_to_categories(label_map, NUM_CLASS) #得到类别
>     category_index = label_map_util.create_category_index(categories) 
>     
>     
>     #2.对视频进行物体检测
>     def detect_objects(image_np, sess, detection_graph):
>         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')
>     
>         #Actual detection
>         (boxes, scores, classes, num_detections) = sess.run(
>             [boxes, scores, classes, num_detections], feed_dict={image_tensor : image_np_expanded})
>     
>         #Visualization of the results of a detection
>         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)
>         return image_np
>     
>     from moviepy.editor import VideoFileClip
>     from IPython.display import HTML
>     
>     def process_image(image):
>         with detection_graph.as_default():
>             with tf.Session(graph=detection_graph) as sess:
>                 image_process = detect_objects(image, sess, detection_graph)
>                 return image_process
>     
>     white_output = 'video/out.mp4'
>     clip1 = VideoFileClip("video/testvideo.mp4")
>     white_clip = clip1.fl_image(process_image)  #This function expects color images!
>     white_clip.write_videofile(white_output, audio=False)

> ### 三、电脑摄像头 ###
> 
>     import cv2
>     import numpy as np
>     import tensorflow as tf
>     from PIL import Image
>     from tensorflow.models.research.object_detection.utils import label_map_util
>     from tensorflow.models.research.object_detection.utils import visualization_utils as vis_util
>     
>     camera = cv2.VideoCapture(0)
>     cv2.namedWindow('MyCamera')
>     
>     PB_PATH = 'ssd_inception_v2_coco_2018_01_28/frozen_inference_graph.pb'
>     LABEL_PATH = 'data/mscoco_label_map.pbtxt'
>     
>     od_graph = tf.Graph()
>     with od_graph.as_default():
>         od_graph_def = tf.GraphDef()
>         # 打开文件，rb是读取二进制，同open()
>         with tf.gfile.FastGFile(PB_PATH, 'rb') as f:
>             od_graph_def.ParseFromString(f.read())
>             tf.import_graph_def(od_graph_def, name="")
>     
>     category_index = label_map_util.create_category_index_from_labelmap(LABEL_PATH)
>     
>     test_images_path = ["test_images/image{0}.jpg".format(i) for i in range(1, 4)]
>     
>     def image_to_np(image):
>         (im_w, im_h) = image.size
>         return np.array(image.getdata()).reshape(im_h, im_w, 3).astype(np.uint8)
>     
>     def run_inference_for_single_image(image):
>         with od_graph.as_default():
>             with tf.Session() as sess:
>                 tensor_dict = {}
>                 tensor_dict['num_detections'] = tf.get_default_graph().get_tensor_by_name('num_detections:0')
>                 tensor_dict['detection_boxes'] = tf.get_default_graph().get_tensor_by_name('detection_boxes:0')
>                 tensor_dict['detection_scores'] = tf.get_default_graph().get_tensor_by_name('detection_scores:0')
>                 tensor_dict['detection_classes'] = tf.get_default_graph().get_tensor_by_name('detection_classes:0')
>                 image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
>     
>                 output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image})
>     
>                 output_dict['num_detections'] = int(output_dict['num_detections'][0])
>                 output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
>                 output_dict['detection_scores'] = output_dict['detection_scores'][0]
>                 output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.int64)
>                 return output_dict
>     
>     while True:
>         success, image = camera.read()
>         image_np_dim = np.expand_dims(image, axis=0)
>         output_dict = run_inference_for_single_image(image_np_dim)
>     
>         vis_util.visualize_boxes_and_labels_on_image_array(
>             image,
>             output_dict['detection_boxes'],
>             output_dict['detection_classes'],
>             output_dict['detection_scores'],
>             category_index,
>             use_normalized_coordinates=True)
>         cv2.imshow('object detection', cv2.resize(image, (800,600)))
>         if cv2.waitKey(1) & 0xff == ord('q'):
>             break
>     
>     
>     cv2.destroyWindow('MyCamera')
>     camera.release()
> 
>  **如果报错，检查一下路径名是否有中文，检查一下摄像头权限是否打开。**

[Link 1]: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md