2019.6.22 实时视频检测

实时视频检测，用下载好的模型
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import matplotlib
import cv2

Matplotlib chooses Xwindows backend by default.

matplotlib.use(‘Agg’)

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

‘’’
检测视频中的目标
‘’’

cap = cv2.VideoCapture(0) # 打开摄像头

##################### Download Model

What model to download.

MODEL_NAME = ‘ssd_mobilenet_v1_coco_2017_11_17’
MODEL_FILE = MODEL_NAME + ‘.tar.gz’
DOWNLOAD_BASE = ‘http://download.tensorflow.org/models/object_detection/’

Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT =‘C:\Users\Administrator\Desktop\deep learning\ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb’

List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join(‘E:\anaconda\envs\tensorflow-opencv\models\research\object_detection\data’, ‘mscoco_label_map.pbtxt’)

NUM_CLASSES = 90

Download model if not already downloaded

if not os.path.exists(PATH_TO_CKPT):
print(‘Downloading model… (This may take over 5 minutes)’)
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
print(‘Extracting…’)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if ‘frozen_inference_graph.pb’ in file_name:
tar_file.extract(file, os.getcwd())
else:
print(‘Model already downloaded.’)

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##################### Load a (frozen) Tensorflow model into memory.
print(‘Loading model…’)
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=’’)

##################### Loading label map

print(‘Loading label map…’)
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)

##################### Helper code
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)

##################### Detection ###########

print(‘Detecting…’)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:

    # print(TEST_IMAGE_PATH)
    # image = Image.open(TEST_IMAGE_PATH)
    # image_np = load_image_into_numpy_array(image)
    while True:
        ret, image_np = cap.read()  # 从摄像头中获取每一帧图像
        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})
        # Print the results of a detection.
        print(scores)
        print(classes)
        print(category_index)
        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.resize(image_np, (800, 600)))
        # cv2.waitKey(0)
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break