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- #! /usr/bin/env python
- # -*- coding: utf-8 -*-
- import os
- import sys
- import cv2
- import time
- import numpy as np
- import core.utils as utils
- import tensorflow as tf
- from PIL import Image
-
-
- if __name__ == '__main__':
- pb_file = "./checkpoint/yolov4.pb"
- video_path = "./data/images/road.mp4"
- # video_path = 0
-
- num_classes = 80
- input_size = 416
- score_thresh = 0.3
-
- iou_type = 'diou' #yolov4:diou, else giou
- iou_thresh = 0.45
-
- graph = tf.Graph()
- return_elements = ["input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0", "pred_lbbox/concat_2:0"]
- return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
-
- with tf.Session(graph=graph) as sess:
- vid = cv2.VideoCapture(video_path)
- while True:
- return_value, frame = vid.read()
- if return_value:
- image = Image.fromarray(frame)
- frame_size = frame.shape[:2]
- image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size])
- image_data = image_data[np.newaxis, ...]
- prev_time = time.time()
-
- pred_sbbox, pred_mbbox, pred_lbbox = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]],
- feed_dict={return_tensors[0]: image_data})
-
- pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)), np.reshape(pred_mbbox, (-1, 5 + num_classes)),
- np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
-
- bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, score_thresh)
- bboxes = utils.nms(bboxes, iou_type, iou_thresh, method='nms')
- image = utils.draw_bbox(frame, bboxes)
-
- curr_time = time.time()
- exec_time = curr_time - prev_time
-
- result = np.asarray(image)
- info = "time: %.2f ms" % (1000 * exec_time)
- cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
- result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
- cv2.imshow("result", result)
- if cv2.waitKey(1) & 0xFF == ord('q'):
- break
-
- else:
- print('Finish processing!')
- raise ValueError("No image!")
- break
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