X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Ftools%2Feval.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Ftools%2Feval.py;h=e496e308cfe11ecb083dbc7459af2e0c76afafb8;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/tools/eval.py b/example-apps/PDD/pcb-defect-detection/tools/eval.py new file mode 100755 index 0000000..e496e30 --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/tools/eval.py @@ -0,0 +1,201 @@ +# -*- 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) + + + + + + + + + + + + + + + +