使用yolo v3检测标示视频

import os
import cv2
import time
import tensorflow as tf
from absl import app, flags, logging
from absl.flags import FLAGS
from yolov3_tf2.models import YoloV3, YoloV3Tiny
from yolov3_tf2.dataset import transform_images
from yolov3_tf2.utils import draw_outputs

flags.DEFINE_string('classes', './data/coco.names', 'path to classes file')
flags.DEFINE_string('weights', './checkpoints/yolov3.tf','path to weights file')
flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_string('video', './data/input01.mp4','path to video file or number for webcam)')
flags.DEFINE_string('output', None, 'path to output video')
flags.DEFINE_string('output_format', 'XVID', 'codec used in VideoWriter when saving video to file')
flags.DEFINE_integer('num_classes', 80, 'number of classes in the model')

def main(_argv):

    physical_devices = tf.config.experimental.list_physical_devices('GPU')
    if len(physical_devices) > 0:
        tf.config.experimental.set_memory_growth(physical_devices[0], True)
    if FLAGS.tiny:
        yolo = YoloV3Tiny(classes=FLAGS.num_classes)
    else:
        yolo = YoloV3(classes=FLAGS.num_classes)

    yolo.load_weights(FLAGS.weights)
    logging.info('weights loaded')

    class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
    logging.info('classes loaded')

    times = []

    try:
        vid = cv2.VideoCapture(int(FLAGS.video))
    except:
        vid = cv2.VideoCapture(FLAGS.video)

    out = None

    if FLAGS.output:
        # by default VideoCapture returns float instead of int
        width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = int(vid.get(cv2.CAP_PROP_FPS))
        codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)
        out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))

    while True:
        _, img = vid.read()

        if img is None:
            logging.warning("Empty Frame")
            time.sleep(0.1)
            continue

        img_in = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
        img_in = tf.expand_dims(img_in, 0)
        img_in = transform_images(img_in, FLAGS.size)

        t1 = time.time()
        boxes, scores, classes, nums = yolo.predict(img_in)
        t2 = time.time()
        times.append(t2-t1)
        times = times[-20:]

        img = draw_outputs(img, (boxes, scores, classes, nums), class_names)
        img = cv2.putText(img, "Time: {:.2f}ms".format(sum(times)/len(times)*1000), (0, 30),
                          cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
        if FLAGS.output:
            out.write(img)
        cv2.imshow('output', img)
        if cv2.waitKey(1) == ord('q'):
            break

    cv2.destroyAllWindows()

if __name__ == '__main__':
    while True:
        STRfilename=input("请输入待检测视频名称（Quit退出）")
        if STRfilename.upper()=="QUIT":
            break
        else:
            STRfilename="./data/"+STRfilename
            if not os.path.exists(STRfilename):
                continue
            ### flags_dict = FLAGS._flags()    
            ### keys_list = [keys for keys in flags_dict]    
            ### for keys in keys_list:
            ###     FLAGS.__delattr__(keys)
            FLAGS.__delattr__("video")
            flags.DEFINE_string('video', STRfilename, 'path to video file or number for webcam)')
        try:
            app.run(main)
        except SystemExit:
            pass