Tensorflow2.0 YOLOv3 구현(6): dataset.py
82946 단어 tensorflowYOLOv3
문서 목록
문장 설명
이 시리즈는 Github에서malin9402가 제공한 코드에 대한 설명을 목적으로 하고 있으며, 이 글은 YOLOv3 프로젝트의 데이터set에 대한 설명을 드리겠습니다.py 파일에 대한 설명입니다.이 파일에는 데이터 집합을 만드는 데 사용되는 Dataset 클래스가 하나밖에 없기 때문에 본고는 코드에 대한 설명을 코드 옆에 직접 씁니다.
Github의 코드만 실행하고 싶으면 YOLOv3 코드에 대한 설명을 참고하십시오.
전체 코드 import os
import cv2
import random
import numpy as np
import tensorflow as tf
import core.utils as utils
from core.config import cfg
class Dataset(object):
"""implement Dataset here"""
def __init__(self, dataset_type):
self.annot_path = cfg.TRAIN.ANNOT_PATH if dataset_type == 'train' else cfg.TEST.ANNOT_PATH # （ ）
self.input_sizes = cfg.TRAIN.INPUT_SIZE if dataset_type == 'train' else cfg.TEST.INPUT_SIZE # （ ）
self.batch_size = cfg.TRAIN.BATCH_SIZE if dataset_type == 'train' else cfg.TEST.BATCH_SIZE # （ ）
self.data_aug = cfg.TRAIN.DATA_AUG if dataset_type == 'train' else cfg.TEST.DATA_AUG # （ ）
self.train_input_sizes = cfg.TRAIN.INPUT_SIZE #
self.strides = np.array(cfg.YOLO.STRIDES) # feature map
self.classes = utils.read_class_names(cfg.YOLO.CLASSES) #
self.num_classes = len(self.classes) #
self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS)) #
self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE # （feature map）
self.max_bbox_per_scale = 150 # （feature map） （ ）
self.annotations = self.load_annotations(dataset_type) # （ ）
self.num_samples = len(self.annotations) #
self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) # batch
self.batch_count = 0 #
def load_annotations(self, dataset_type):
with open(self.annot_path, 'r') as f:
txt = f.readlines()
annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0]
np.random.shuffle(annotations)
return annotations
def __iter__(self):
return self
def __next__(self):
with tf.device('/cpu:0'):
self.train_input_size = random.choice(self.train_input_sizes) # ， ； ，
self.train_output_sizes = self.train_input_size // self.strides # （ feature map）
#
batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3), dtype=np.float32)
# （ 4， 52，26，13， 3 ， 80 ）
batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 52, 52, 3, 85]
batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 26, 26, 3, 85]
batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 13, 13, 3, 85]
batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
num = 0 # （4 ）
if self.batch_count < self.num_batchs: # batch batch
while num < self.batch_size: # （4 ）
index = self.batch_count * self.batch_size + num # （ ）
if index >= self.num_samples: index -= self.num_samples # ， index ， repeat
annotation = self.annotations[index] # index （ shuffle ， index.jpg）
image, bboxes = self.parse_annotation(annotation) # -> ->
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes) #
batch_image[num, :, :, :] = image #
batch_label_sbbox[num, :, :, :, :] = label_sbbox #
batch_label_mbbox[num, :, :, :, :] = label_mbbox #
batch_label_lbbox[num, :, :, :, :] = label_lbbox #
batch_sbboxes[num, :, :] = sbboxes #
batch_mbboxes[num, :, :] = mbboxes #
batch_lbboxes[num, :, :] = lbboxes #
num += 1
self.batch_count += 1
batch_smaller_target = batch_label_sbbox, batch_sbboxes #
batch_medium_target = batch_label_mbbox, batch_mbboxes #
batch_larger_target = batch_label_lbbox, batch_lbboxes #
return batch_image, (batch_smaller_target, batch_medium_target, batch_larger_target)
else: # batch batch
self.batch_count = 0 #
np.random.shuffle(self.annotations) #
raise StopIteration
def random_horizontal_flip(self, image, bboxes): #
if random.random() < 0.5: # 0.5
_, w, _ = image.shape
image = image[:, ::-1, :]
bboxes[:, [0,2]] = w - bboxes[:, [2,0]]
return image, bboxes
def random_crop(self, image, bboxes): #
if random.random() < 0.5: # 0.5
h, w, _ = image.shape #
# [xmin, ymin] [xmin, ymin] [xmax, ymax] [xmax, ymax]
# max_bbox
max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
max_l_trans = max_bbox[0] #
max_u_trans = max_bbox[1] #
max_r_trans = w - max_bbox[2] #
max_d_trans = h - max_bbox[3] #
crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans))) #
crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans))) #
crop_xmax = max(w, int(max_bbox[2] + random.uniform(0, max_r_trans))) #
crop_ymax = max(h, int(max_bbox[3] + random.uniform(0, max_d_trans))) #
image = image[crop_ymin : crop_ymax, crop_xmin : crop_xmax] #
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin #
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin #
return image, bboxes
def random_translate(self, image, bboxes): #
if random.random() < 0.5: # 0.5
h, w, _ = image.shape #
# [xmin, ymin] [xmin, ymin] [xmax, ymax] [xmax, ymax]
# max_bbox
max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
max_l_trans = max_bbox[0] #
max_u_trans = max_bbox[1] #
max_r_trans = w - max_bbox[2] #
max_d_trans = h - max_bbox[3] #
tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) #
ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) #
M = np.array([[1, 0, tx], [0, 1, ty]])
image = cv2.warpAffine(image, M, (w, h)) #
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx #
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty #
return image, bboxes
def parse_annotation(self, annotation):
line = annotation.split() # ' '
image_path = line[0] #
if not os.path.exists(image_path): #
raise KeyError("%s does not exist ... " %image_path)
image = cv2.imread(image_path) #
# map ， （ text.py ）
bboxes = np.array([list(map(int, box.split(','))) for box in line[1:]])
if self.data_aug: #
image, bboxes = self.random_horizontal_flip(np.copy(image), np.copy(bboxes)) #
image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) #
image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes)) #
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # BGR --> RGB
#
image, bboxes = utils.image_preporcess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes))
return image, bboxes
# IOU
def bbox_iou(self, boxes1, boxes2):
boxes1 = np.array(boxes1) #
boxes2 = np.array(boxes2) #
boxes1_area = boxes1[..., 2] * boxes1[..., 3] #
boxes2_area = boxes2[..., 2] * boxes2[..., 3] #
boxes1 = np.concatenate([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) # +
boxes2 = np.concatenate([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) # +
left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) # ，left_up=[xmin2, ymin2]
right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) # ，right_down=[xmax1, ymax1]
inter_section = np.maximum(right_down - left_up, 0.0) #
inter_area = inter_section[..., 0] * inter_section[..., 1] #
union_area = boxes1_area + boxes2_area - inter_area #
return inter_area / union_area
def preprocess_true_boxes(self, bboxes):
# label 3 ， [ feature map , feature map , feature map , 5 + ]
label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale,
5 + self.num_classes)) for i in range(3)]
# bboxes_xywh 3 ， [ , 4 ]
bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(3)]
bbox_count = np.zeros((3,)) #
for bbox in bboxes: #
bbox_coor = bbox[:4] # （ + ）
bbox_class_ind = bbox[4] # （ ）
onehot = np.zeros(self.num_classes, dtype=np.float) #
onehot[bbox_class_ind] = 1.0 # 1
# uniform_distribution [ ,] ，
uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes)
deta = 0.01
smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution #
# [xmin, ymin, xmax, ymax] [ , , , ]
bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1)
# feature map （ ）
bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] # bbox_xywh shape: [1, 4]；strides shape: [3, ]; bbox_xywh_scaled shape: [1, 3, 4]
iou = []
exist_positive = False # False
for i in range(3): # 3
anchors_xywh = np.zeros((self.anchor_per_scale, 4)) # ，shape = [3, 4]，3 ，4
anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 # （ ）
anchors_xywh[:, 2:4] = self.anchors[i] #
iou_scale = self.bbox_iou(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh) # （ ） IOU
iou.append(iou_scale)
iou_mask = iou_scale > 0.3 # IOU 0.3 1
if np.any(iou_mask): #
xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) #
# (yind, xind)
label[i][yind, xind, iou_mask, :] = 0
label[i][yind, xind, iou_mask, 0:4] = bbox_xywh #
label[i][yind, xind, iou_mask, 4:5] = 1.0 # 1
label[i][yind, xind, iou_mask, 5:] = smooth_onehot #
bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) #
bboxes_xywh[i][bbox_ind, :4] = bbox_xywh #
bbox_count[i] += 1
exist_positive = True # True
if not exist_positive: #
best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) # IOU
best_detect = int(best_anchor_ind / self.anchor_per_scale) #
best_anchor = int(best_anchor_ind % self.anchor_per_scale) #
xind, yind = np.floor(bbox_xywh_scaled[best_detect, 0:2]).astype(np.int32) #
label[best_detect][yind, xind, best_anchor, :] = 0
label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh #
label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 # 1
label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot #
bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale) #
bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh #
bbox_count[best_detect] += 1
label_sbbox, label_mbbox, label_lbbox = label # （ 、 ）
sbboxes, mbboxes, lbboxes = bboxes_xywh #
return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __len__(self):
return self.num_batchs
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import os
import cv2
import random
import numpy as np
import tensorflow as tf
import core.utils as utils
from core.config import cfg
class Dataset(object):
"""implement Dataset here"""
def __init__(self, dataset_type):
self.annot_path = cfg.TRAIN.ANNOT_PATH if dataset_type == 'train' else cfg.TEST.ANNOT_PATH # （ ）
self.input_sizes = cfg.TRAIN.INPUT_SIZE if dataset_type == 'train' else cfg.TEST.INPUT_SIZE # （ ）
self.batch_size = cfg.TRAIN.BATCH_SIZE if dataset_type == 'train' else cfg.TEST.BATCH_SIZE # （ ）
self.data_aug = cfg.TRAIN.DATA_AUG if dataset_type == 'train' else cfg.TEST.DATA_AUG # （ ）
self.train_input_sizes = cfg.TRAIN.INPUT_SIZE #
self.strides = np.array(cfg.YOLO.STRIDES) # feature map
self.classes = utils.read_class_names(cfg.YOLO.CLASSES) #
self.num_classes = len(self.classes) #
self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS)) #
self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE # （feature map）
self.max_bbox_per_scale = 150 # （feature map） （ ）
self.annotations = self.load_annotations(dataset_type) # （ ）
self.num_samples = len(self.annotations) #
self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) # batch
self.batch_count = 0 #
def load_annotations(self, dataset_type):
with open(self.annot_path, 'r') as f:
txt = f.readlines()
annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0]
np.random.shuffle(annotations)
return annotations
def __iter__(self):
return self
def __next__(self):
with tf.device('/cpu:0'):
self.train_input_size = random.choice(self.train_input_sizes) # ， ； ，
self.train_output_sizes = self.train_input_size // self.strides # （ feature map）
#
batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3), dtype=np.float32)
# （ 4， 52，26，13， 3 ， 80 ）
batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 52, 52, 3, 85]
batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 26, 26, 3, 85]
batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2],
self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) # ，shape [4, 13, 13, 3, 85]
batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
num = 0 # （4 ）
if self.batch_count < self.num_batchs: # batch batch
while num < self.batch_size: # （4 ）
index = self.batch_count * self.batch_size + num # （ ）
if index >= self.num_samples: index -= self.num_samples # ， index ， repeat
annotation = self.annotations[index] # index （ shuffle ， index.jpg）
image, bboxes = self.parse_annotation(annotation) # -> ->
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes) #
batch_image[num, :, :, :] = image #
batch_label_sbbox[num, :, :, :, :] = label_sbbox #
batch_label_mbbox[num, :, :, :, :] = label_mbbox #
batch_label_lbbox[num, :, :, :, :] = label_lbbox #
batch_sbboxes[num, :, :] = sbboxes #
batch_mbboxes[num, :, :] = mbboxes #
batch_lbboxes[num, :, :] = lbboxes #
num += 1
self.batch_count += 1
batch_smaller_target = batch_label_sbbox, batch_sbboxes #
batch_medium_target = batch_label_mbbox, batch_mbboxes #
batch_larger_target = batch_label_lbbox, batch_lbboxes #
return batch_image, (batch_smaller_target, batch_medium_target, batch_larger_target)
else: # batch batch
self.batch_count = 0 #
np.random.shuffle(self.annotations) #
raise StopIteration
def random_horizontal_flip(self, image, bboxes): #
if random.random() < 0.5: # 0.5
_, w, _ = image.shape
image = image[:, ::-1, :]
bboxes[:, [0,2]] = w - bboxes[:, [2,0]]
return image, bboxes
def random_crop(self, image, bboxes): #
if random.random() < 0.5: # 0.5
h, w, _ = image.shape #
# [xmin, ymin] [xmin, ymin] [xmax, ymax] [xmax, ymax]
# max_bbox
max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
max_l_trans = max_bbox[0] #
max_u_trans = max_bbox[1] #
max_r_trans = w - max_bbox[2] #
max_d_trans = h - max_bbox[3] #
crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans))) #
crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans))) #
crop_xmax = max(w, int(max_bbox[2] + random.uniform(0, max_r_trans))) #
crop_ymax = max(h, int(max_bbox[3] + random.uniform(0, max_d_trans))) #
image = image[crop_ymin : crop_ymax, crop_xmin : crop_xmax] #
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin #
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin #
return image, bboxes
def random_translate(self, image, bboxes): #
if random.random() < 0.5: # 0.5
h, w, _ = image.shape #
# [xmin, ymin] [xmin, ymin] [xmax, ymax] [xmax, ymax]
# max_bbox
max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
max_l_trans = max_bbox[0] #
max_u_trans = max_bbox[1] #
max_r_trans = w - max_bbox[2] #
max_d_trans = h - max_bbox[3] #
tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) #
ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) #
M = np.array([[1, 0, tx], [0, 1, ty]])
image = cv2.warpAffine(image, M, (w, h)) #
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx #
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty #
return image, bboxes
def parse_annotation(self, annotation):
line = annotation.split() # ' '
image_path = line[0] #
if not os.path.exists(image_path): #
raise KeyError("%s does not exist ... " %image_path)
image = cv2.imread(image_path) #
# map ， （ text.py ）
bboxes = np.array([list(map(int, box.split(','))) for box in line[1:]])
if self.data_aug: #
image, bboxes = self.random_horizontal_flip(np.copy(image), np.copy(bboxes)) #
image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) #
image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes)) #
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # BGR --> RGB
#
image, bboxes = utils.image_preporcess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes))
return image, bboxes
# IOU
def bbox_iou(self, boxes1, boxes2):
boxes1 = np.array(boxes1) #
boxes2 = np.array(boxes2) #
boxes1_area = boxes1[..., 2] * boxes1[..., 3] #
boxes2_area = boxes2[..., 2] * boxes2[..., 3] #
boxes1 = np.concatenate([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) # +
boxes2 = np.concatenate([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) # +
left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) # ，left_up=[xmin2, ymin2]
right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) # ，right_down=[xmax1, ymax1]
inter_section = np.maximum(right_down - left_up, 0.0) #
inter_area = inter_section[..., 0] * inter_section[..., 1] #
union_area = boxes1_area + boxes2_area - inter_area #
return inter_area / union_area
def preprocess_true_boxes(self, bboxes):
# label 3 ， [ feature map , feature map , feature map , 5 + ]
label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale,
5 + self.num_classes)) for i in range(3)]
# bboxes_xywh 3 ， [ , 4 ]
bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(3)]
bbox_count = np.zeros((3,)) #
for bbox in bboxes: #
bbox_coor = bbox[:4] # （ + ）
bbox_class_ind = bbox[4] # （ ）
onehot = np.zeros(self.num_classes, dtype=np.float) #
onehot[bbox_class_ind] = 1.0 # 1
# uniform_distribution [ ,] ，
uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes)
deta = 0.01
smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution #
# [xmin, ymin, xmax, ymax] [ , , , ]
bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1)
# feature map （ ）
bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] # bbox_xywh shape: [1, 4]；strides shape: [3, ]; bbox_xywh_scaled shape: [1, 3, 4]
iou = []
exist_positive = False # False
for i in range(3): # 3
anchors_xywh = np.zeros((self.anchor_per_scale, 4)) # ，shape = [3, 4]，3 ，4
anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 # （ ）
anchors_xywh[:, 2:4] = self.anchors[i] #
iou_scale = self.bbox_iou(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh) # （ ） IOU
iou.append(iou_scale)
iou_mask = iou_scale > 0.3 # IOU 0.3 1
if np.any(iou_mask): #
xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) #
# (yind, xind)
label[i][yind, xind, iou_mask, :] = 0
label[i][yind, xind, iou_mask, 0:4] = bbox_xywh #
label[i][yind, xind, iou_mask, 4:5] = 1.0 # 1
label[i][yind, xind, iou_mask, 5:] = smooth_onehot #
bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) #
bboxes_xywh[i][bbox_ind, :4] = bbox_xywh #
bbox_count[i] += 1
exist_positive = True # True
if not exist_positive: #
best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) # IOU
best_detect = int(best_anchor_ind / self.anchor_per_scale) #
best_anchor = int(best_anchor_ind % self.anchor_per_scale) #
xind, yind = np.floor(bbox_xywh_scaled[best_detect, 0:2]).astype(np.int32) #
label[best_detect][yind, xind, best_anchor, :] = 0
label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh #
label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 # 1
label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot #
bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale) #
bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh #
bbox_count[best_detect] += 1
label_sbbox, label_mbbox, label_lbbox = label # （ 、 ）
sbboxes, mbboxes, lbboxes = bboxes_xywh #
return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __len__(self):
return self.num_batchs
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