Created
July 17, 2018 10:22
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Faster NMS
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| # coding=utf-8 | |
| import copy | |
| import time | |
| import numpy as np | |
| import cv2 | |
| from lib.yolo.detector import YOLO | |
| from lib.yolo.utilities import root_path, BoundBox, bbox_iou, decode_netout, correct_yolo_boxes | |
| def old_do_nms(boxes, nms_thresh): | |
| if len(boxes) > 0: | |
| nb_class = len(boxes[0].classes) | |
| else: | |
| return | |
| for c in range(nb_class): | |
| sorted_indices = np.argsort([-box.classes[c] for box in boxes]) | |
| for i in range(len(sorted_indices)): | |
| index_i = sorted_indices[i] | |
| if boxes[index_i].classes[c] == 0: continue | |
| for j in range(i + 1, len(sorted_indices)): | |
| index_j = sorted_indices[j] | |
| if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh: | |
| boxes[index_j].classes[c] = 0 | |
| def new_do_nms(boxes, nms_thresh): | |
| if len(boxes) > 0: | |
| nb_class = len(boxes[0].classes) | |
| else: | |
| return | |
| for c in range(nb_class): | |
| class_max_boxes = np.max([box.classes[c] for box in boxes]) | |
| if class_max_boxes == 0: continue | |
| sorted_indices = np.argsort([-box.classes[c] for box in boxes]) | |
| for i in range(len(sorted_indices)): | |
| index_i = sorted_indices[i] | |
| if boxes[index_i].classes[c] == 0: continue | |
| for j in range(i + 1, len(sorted_indices)): | |
| index_j = sorted_indices[j] | |
| if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh: | |
| boxes[index_j].classes[c] = 0 | |
| weight_path = root_path('yolo', 'model', 'weights_fashion_v31.pb') | |
| yolo = YOLO(weight_path) | |
| img_path = root_path('yolo', 'test', 'images', 'n03589791_10279.JPEG') | |
| image = cv2.imread(img_path) | |
| image_h, image_w, _ = image.shape | |
| output = yolo.predict(image, decode=False) | |
| boxes = [] | |
| for i in range(len(output)): | |
| yolo_anchors = yolo._anchors[(2 - i) * 6:(3 - i) * 6] | |
| boxes += decode_netout( | |
| np.squeeze(output[i], axis=0), | |
| yolo_anchors, | |
| yolo._obj_threshold, | |
| yolo._net_h, | |
| yolo._net_w) | |
| # correct the sizes of the bounding boxes | |
| correct_yolo_boxes(boxes, image_h, image_w, yolo._net_h, yolo._net_w) | |
| copy_boxes = copy.deepcopy(boxes) | |
| start = time.time() | |
| for i in range(1000): | |
| new_do_nms(copy_boxes, 0.1) | |
| print(time.time() - start) | |
| start = time.time() | |
| for i in range(1000): | |
| old_do_nms(boxes, 0.1) | |
| print(time.time() - start) | |
| 0.5759725570678711 | |
| 1.6929998397827148 |
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