+++ /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 pickle
-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.val_libs import voc_eval
-from libs.box_utils import draw_box_in_img
-import argparse
-from help_utils import tools
-
-
-def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False):
-
- # 1. preprocess img
- img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR
- 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)
-
- 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
-
- compute_time = 0
- compute_imgnum = 0
-
- with tf.Session(config=config) as sess:
- sess.run(init_op)
- if not restorer is None:
- restorer.restore(sess, restore_ckpt)
- print('restore model')
-
- all_boxes = []
- for i, a_img_name in enumerate(real_test_imgname_list):
-
- raw_img = cv2.imread(os.path.join(img_root, a_img_name))
- raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
-
- 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()
- compute_time = compute_time + (end - start)
- compute_imgnum = compute_imgnum + 1
- # print("{} cost time : {} ".format(img_name, (end - start)))
- if draw_imgs:
- 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(np.squeeze(resized_img, 0),
- boxes=show_boxes,
- labels=show_categories,
- scores=show_scores)
- if not os.path.exists(cfgs.TEST_SAVE_PATH):
- os.makedirs(cfgs.TEST_SAVE_PATH)
-
- cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg',
- final_detections[:, :, ::-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
-
- boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
- dets = np.hstack((detected_categories.reshape(-1, 1),
- detected_scores.reshape(-1, 1),
- boxes))
- all_boxes.append(dets)
-
- tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list))
-
- # save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)
- # if not os.path.exists(save_dir):
- # os.makedirs(save_dir)
- # fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb')
- # pickle.dump(all_boxes, fw1)
- print('\n average_training_time_per_image is' + str(compute_time / compute_imgnum))
- return all_boxes
-
-
-def eval(num_imgs, eval_dir, annotation_dir, showbox):
-
- # with open('/home/yjr/DataSet/VOC/VOC_test/VOC2007/ImageSets/Main/aeroplane_test.txt') as f:
- # all_lines = f.readlines()
- # test_imgname_list = [a_line.split()[0].strip() for a_line in all_lines]
-
- test_imgname_list = [item for item in os.listdir(eval_dir)
- if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff'))]
- if num_imgs == np.inf:
- real_test_imgname_list = test_imgname_list
- else:
- real_test_imgname_list = test_imgname_list[: num_imgs]
-
- faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME,
- is_training=False)
- all_boxes = eval_with_plac(det_net=faster_rcnn, real_test_imgname_list=real_test_imgname_list,
- img_root=eval_dir,
- draw_imgs=showbox)
-
- # save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)
- # if not os.path.exists(save_dir):
- # os.makedirs(save_dir)
- # with open(os.path.join(save_dir, 'detections.pkl'), 'rb') as f:
- # all_boxes = pickle.load(f)
- #
- # print(len(all_boxes))
-
- voc_eval.voc_evaluate_detections(all_boxes=all_boxes,
- test_annotation_path=annotation_dir,
- test_imgid_list=real_test_imgname_list)
-
-def parse_args():
-
- parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand')
-
- parser.add_argument('--eval_imgs', dest='eval_imgs',
- help='evaluate imgs dir ',
- default='../data/pcb_test/JPEGImages', type=str)
- parser.add_argument('--annotation_dir', dest='test_annotation_dir',
- help='the dir save annotations',
- default='../data/pcb_test/Annotations', type=str)
- parser.add_argument('--showbox', dest='showbox',
- help='whether show detecion results when evaluation',
- default=False, type=bool)
- parser.add_argument('--GPU', dest='GPU',
- help='gpu id',
- default='2', type=str)
- #parser.add_argument('--eval_num', dest='eval_num',
- # help='the num of eval imgs',
- # default=np.inf, type=int)
- parser.add_argument('--eval_num', dest='eval_num',
- help='the num of eval imgs',
- default=100, type=int)
- args = parser.parse_args()
- return args
-
-
-if __name__ == '__main__':
-
- args = parse_args()
- print(20*"--")
- print(args)
- print(20*"--")
- os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
- eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs.
- eval_dir=args.eval_imgs,
- annotation_dir=args.test_annotation_dir,
- showbox=args.showbox)
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Code Review / ealt-edge.git / blobdiff