--- /dev/null
+# -*- coding:utf-8 -*-
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import os, sys
+import tensorflow as tf
+import time
+import cv2
+import argparse
+import numpy as np
+sys.path.append("../")
+
+from data.io.image_preprocess import short_side_resize_for_inference_data
+from libs.configs import cfgs
+from libs.networks import build_whole_network
+from libs.box_utils import draw_box_in_img
+from help_utils import tools
+
+
+def detect(det_net, inference_save_path, real_test_imgname_list):
+
+ # 1. preprocess img
+ img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR
+ img_batch = tf.cast(img_plac, tf.float32)
+ img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
+ target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
+ length_limitation=cfgs.IMG_MAX_LENGTH)
+ img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
+ img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3]
+
+ detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
+ input_img_batch=img_batch,
+ gtboxes_batch=None)
+
+ init_op = tf.group(
+ tf.global_variables_initializer(),
+ tf.local_variables_initializer()
+ )
+
+ restorer, restore_ckpt = det_net.get_restorer()
+
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+
+ with tf.Session(config=config) as sess:
+ sess.run(init_op)
+ if not restorer is None:
+ restorer.restore(sess, restore_ckpt)
+ print('restore model')
+
+ for i, a_img_name in enumerate(real_test_imgname_list):
+
+ raw_img = cv2.imread(a_img_name)
+ start = time.time()
+ resized_img, detected_boxes, detected_scores, detected_categories = \
+ sess.run(
+ [img_batch, detection_boxes, detection_scores, detection_category],
+ feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
+ )
+ end = time.time()
+ # print("{} cost time : {} ".format(img_name, (end - start)))
+
+ raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
+
+ xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
+ detected_boxes[:, 2], detected_boxes[:, 3]
+
+ resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
+
+ xmin = xmin * raw_w / resized_w
+ xmax = xmax * raw_w / resized_w
+
+ ymin = ymin * raw_h / resized_h
+ ymax = ymax * raw_h / resized_h
+
+ detected_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
+
+ show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
+ show_scores = detected_scores[show_indices]
+ show_boxes = detected_boxes[show_indices]
+ show_categories = detected_categories[show_indices]
+ final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img - np.array(cfgs.PIXEL_MEAN),
+ boxes=show_boxes,
+ labels=show_categories,
+ scores=show_scores)
+ nake_name = a_img_name.split('/')[-1]
+ # print (inference_save_path + '/' + nake_name)
+ cv2.imwrite(inference_save_path + '/' + nake_name,
+ final_detections[:, :, ::-1])
+
+ tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list))
+
+
+def test(test_dir, inference_save_path):
+
+ test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir)
+ if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff'))]
+ assert len(test_imgname_list) != 0, 'test_dir has no imgs there.' \
+ ' Note that, we only support img format of (.jpg, .png, and .tiff) '
+
+ faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME,
+ is_training=False)
+ detect(det_net=faster_rcnn, inference_save_path=inference_save_path, real_test_imgname_list=test_imgname_list)
+
+
+def parse_args():
+ """
+ Parse input arguments
+ """
+ parser = argparse.ArgumentParser(description='TestImgs...U need provide the test dir')
+ parser.add_argument('--data_dir', dest='data_dir',
+ help='data path',
+ default='demos', type=str)
+ parser.add_argument('--save_dir', dest='save_dir',
+ help='demo imgs to save',
+ default='inference_results', type=str)
+ parser.add_argument('--GPU', dest='GPU',
+ help='gpu id ',
+ default='0', type=str)
+
+ if len(sys.argv) == 1:
+ parser.print_help()
+ sys.exit(1)
+
+ args = parser.parse_args()
+
+ return args
+if __name__ == '__main__':
+
+ args = parse_args()
+ print('Called with args:')
+ print(args)
+ os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
+ test(args.data_dir,
+ inference_save_path=args.save_dir)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+