搭建自己的物体检测模型（3）对视频进行检测

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将测试的*.mp4格式原文件放到models\research\object_detection路径下，并创建一个oildrum_video_detection.py文件以及oildrum_detection_cramea.py文件即可。

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1、对视频的检测

oildrum_video_detection.py文件内容：

import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
import matplotlib
# Matplotlib chooses Xwindows backend by default.
matplotlib.use('Agg')

import label_map_util
import visualization_utils as vis_util



######################这个里面修改三处##########################################
# Path to frozen detection graph. This is the actual model that is used for the object detection.
MODEL_NAME = 'oildrum_detection'
PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb')

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'oildrum.pbtxt')


NUM_CLASSES = 1
############################################################################


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)

def detect_objects(image_np, sess, detection_graph):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Each box represents a part of the image where a particular object was detected.
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    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

#Load a frozen TF 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='')



import imageio
#imageio.plugins.ffmpeg.download()
 # Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

def process_image(image):
    # NOTE: The output you return should be a color image (3 channel) for processing video below
    # you should return the final output (image with lines are drawn on lanes)
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            image_process = detect_objects(np.array(image), sess, detection_graph)
            return image_process
        
        
############写自己的视频，（处理的秒数）########################################
white_output = 'oildrum_video_out1.mp4'
clip1 = VideoFileClip("oildrum_video1.mp4").subclip(1,15)
#####################################################


white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s
white_clip.write_videofile(white_output, audio=False)

HTML("""

""".format(white_output))

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2、调用笔记本前置摄像头来实时进行检测

oildrum_detection_cramea.py文件内容：

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
import label_map_util
import visualization_utils as vis_util



cap = cv2.VideoCapture(0)  # 打开摄像头

################################################################change3处#######
# 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 = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('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.')

##################### 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

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参考文章：
https://blog.csdn.net/gulingfengze/article/details/79690465