--- /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)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Code Review / ealt-edge.git / blobdiff