--- /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("../")
+sys.path.insert(0, '/home/yjr/PycharmProjects/Faster-RCNN_TF/data/lib_coco/PythonAPI')
+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
+from libs.label_name_dict.coco_dict import LABEL_NAME_MAP, classes_originID
+from help_utils import tools
+from data.lib_coco.PythonAPI.pycocotools.coco import COCO
+import json
+
+os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP
+
+
+def eval_with_plac(det_net, imgId_list, coco, out_json_root, draw_imgs=False):
+
+ # 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)
+
+ 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
+
+ # coco_test_results = []
+
+ 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, imgid in enumerate(imgId_list):
+ imgname = coco.loadImgs(ids=[imgid])[0]['file_name']
+ raw_img = cv2.imread(os.path.join("/home/yjr/DataSet/COCO/2017/test2017", imgname))
+
+ 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()
+
+ 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)
+ cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + str(imgid) + '.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-xmin, ymax-ymin]))
+
+ dets = np.hstack((detected_categories.reshape(-1, 1),
+ detected_scores.reshape(-1, 1),
+ boxes))
+
+ a_img_detect_result = []
+ for a_det in dets:
+ label, score, bbox = a_det[0], a_det[1], a_det[2:]
+ cat_id = classes_originID[LABEL_NAME_MAP[label]]
+ if score<0.00001:
+ continue
+ det_object = {"image_id": imgid,
+ "category_id": cat_id,
+ "bbox": bbox.tolist(),
+ "score": float(score)}
+ # print (det_object)
+ a_img_detect_result.append(det_object)
+ f = open(os.path.join(out_json_root, 'each_img', str(imgid)+'.json'), 'w')
+ json.dump(a_img_detect_result, f) # , indent=4
+ f.close()
+ del a_img_detect_result
+ del dets
+ del boxes
+ del resized_img
+ del raw_img
+ tools.view_bar('{} image cost {}s'.format(imgid, (end - start)), i + 1, len(imgId_list))
+
+
+def eval(num_imgs):
+
+
+ # annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test2017.json'
+ annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test-dev2017.json'
+ # annotation_path = '/home/yjr/DataSet/COCO/2017/annotations/instances_train2017.json'
+ print("load coco .... it will cost about 17s..")
+ coco = COCO(annotation_path)
+
+ imgId_list = coco.getImgIds()
+
+ if num_imgs !=np.inf:
+ imgId_list = imgId_list[: num_imgs]
+
+ faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME,
+ is_training=False)
+ save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)
+ eval_with_plac(det_net=faster_rcnn, coco=coco, imgId_list=imgId_list, out_json_root=save_dir,
+ draw_imgs=True)
+ print("each img over**************")
+
+ final_detections = []
+ with open(os.path.join(save_dir, 'coco2017test_results.json'), 'w') as wf:
+ for imgid in imgId_list:
+ f = open(os.path.join(save_dir, 'each_img', str(imgid)+'.json'))
+ tmp_list = json.load(f)
+ # print (type(tmp_list))
+ final_detections.extend(tmp_list)
+ del tmp_list
+ f.close()
+ json.dump(final_detections, wf)
+
+
+if __name__ == '__main__':
+
+ eval(np.inf)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Code Review / ealt-edge.git / blobdiff