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