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dataset.py
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dataset.py
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#! /usr/bin/env python
# coding=utf-8
import os
import cv2
import random
import numpy as np
import tensorflow as tf
import core.utils as utils
from core.config import cfg
from core.config import args
import time
import math
from numba import jit
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.data_train_flag = True if dataset_type == 'train' else False
self.train_input_sizes = cfg.TRAIN.INPUT_SIZE
self.strides = np.array(cfg.YOLO.STRIDES)
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
self.max_bbox_per_scale = 150
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))
self.batch_count = 0
# only use the image including the labeled instance objects for training
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)
print('###################the total image:', len(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
batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3))
batch_clean_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3))
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))
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))
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))
batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4))
batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4))
batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4))
num = 0
if self.batch_count < self.num_batchs:
# start_time = time.time()
while num < self.batch_size:
index = self.batch_count * self.batch_size + num
if index >= self.num_samples: index -= self.num_samples
annotation = self.annotations[index]
image, bboxes, clean_image = self.parse_annotation(annotation)
label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
batch_image[num, :, :, :] = image
batch_clean_image[num, :, :, :] = clean_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
# end_time = time.time()
# print('method1所用时间:', end_time - start_time)
return batch_image, batch_label_sbbox, batch_label_mbbox, batch_label_lbbox, \
batch_sbboxes, batch_mbboxes, batch_lbboxes, batch_clean_image
else:
self.batch_count = 0
np.random.shuffle(self.annotations)
raise StopIteration
def random_horizontal_flip(self, image, bboxes):
if random.random() < 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:
h, w, _ = image.shape
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:
h, w, _ = image.shape
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)
img_name = image_path.split('/')[-1]
# print(img_name)
image_name = img_name.split('.')[0]
# print(image_name)
image_name_index = img_name.split('.')[1]
# image = np.array(cv2.imread(image_path))
# print('*****************read image***************************')
bboxes = np.array([list(map(lambda x: int(float(x)), box.split(','))) for box in line[1:]])
# Each image has a probability of 2/3 to be randomly added with some kind of fog
if random.randint(0, 2) > 0:
beta = random.randint(0, 9)
beta = 0.01 * beta + 0.05
# load voc_foggy_synthetic image offline (The synthesized code is ./core/data_make.py)
if self.data_train_flag:
img_name = args.vocfog_traindata_dir + image_name \
+ '_' + ("%.2f" % beta) + '.' + image_name_index
else:
img_name = args.vocfog_valdata_dir + image_name \
+ '_' + ("%.2f" % beta) + '.' + image_name_index
foggy_image = cv2.imread(img_name)
if self.data_aug:
if random.random() < 0.5:
_, w, _ = image.shape
image = image[:, ::-1, :]
foggy_image = foggy_image[:, ::-1, :]
bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]]
if random.random() < 0.5:
h, w, _ = image.shape
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]
foggy_image = foggy_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
if random.random() < 0.5:
h, w, _ = image.shape
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))
foggy_image = cv2.warpAffine(foggy_image, M, (w, h))
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty
foggy_image, _ = utils.image_preporcess(np.copy(foggy_image),
[self.train_input_size, self.train_input_size],
np.copy(bboxes))
clean_image, bboxes = utils.image_preporcess(np.copy(image),
[self.train_input_size, self.train_input_size],
np.copy(bboxes))
else:
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))
clean_image, bboxes = utils.image_preporcess(np.copy(image),
[self.train_input_size, self.train_input_size],
np.copy(bboxes))
foggy_image = clean_image
return foggy_image, bboxes, clean_image
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])
right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
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 = [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 = [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
uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes)
deta = 0.01
smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution
bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1)
bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis]
iou = []
exist_positive = False
for i in range(3):
anchors_xywh = np.zeros((self.anchor_per_scale, 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.append(iou_scale)
iou_mask = iou_scale > 0.3
if np.any(iou_mask):
xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32)
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
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
if not exist_positive:
best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1)
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
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