def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, mosaic=False):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
flag = True
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i==0:
np.random.shuffle(annotation_lines)
if mosaic:
if flag and (i+4) < n:
image, box = get_random_data_with_Mosaic(annotation_lines[i:i+4], input_shape)
i = (i+4) % n
else:
image, box = get_random_data(annotation_lines[i], input_shape)
i = (i+1) % n
flag = bool(1-flag)
else:
image, box = get_random_data(annotation_lines[i], input_shape)
i = (i+1) % n
image_data.append(image)
box_data.append(box)
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def generate_arrays_from_file(lines, batch_size, input_shape, num_classes, random=True):
n = len(lines)
i = 0
while 1:
X_train = []
Y_train = []
for _ in range(batch_size):
if i==0:
np.random.shuffle(lines)
annotation_path = lines[i].split(';')[1].split()[0]
img = Image.open(annotation_path)
if random:
img = get_random_data(img, [input_shape[0],input_shape[1]])
else:
img = letterbox_image(img, [input_shape[0],input_shape[1]])
img = np.array(img).astype(np.float32)
img = _preprocess_input(img)
X_train.append(img)
Y_train.append(int(lines[i].split(';')[0]))
i = (i+1) % n
X_train = np.array(X_train)
X_train = X_train.reshape([-1,input_shape[0],input_shape[1],input_shape[2]])
Y_train = np_utils.to_categorical(np.array(Y_train),num_classes=num_classes)
yield (X_train, Y_train)
def generate_arrays_from_file(lines, batch_size, train):
# 获取总长度
n = len(lines)
i = 0
while 1:
X_train = []
Y_train = []
# 获取一个batch_size大小的数据
for b in range(batch_size):
if i == 0:
np.random.shuffle(lines)
name = lines[i].split(';')[0]
# 从文件中读取图像
img = Image.open(r".\data\image\train" + '/' + name)
if train == True:
img = np.array(get_random_data(img, [HEIGHT, WIDTH]),
dtype=np.float64)
else:
img = np.array(letterbox_image(img, [HEIGHT, WIDTH]),
dtype=np.float64)
X_train.append(img)
Y_train.append(lines[i].split(';')[1])
# 读完一个周期后重新开始
i = (i + 1) % n
# 处理图像
X_train = preprocess_input(
np.array(X_train).reshape(-1, HEIGHT, WIDTH, 3))
Y_train = np_utils.to_categorical(np.array(Y_train),
num_classes=NUM_CLASSES)
yield (X_train, Y_train)
def data_generator(annotation_lines,
batch_size,
input_shape,
anchors,
num_classes,
random=True):
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
#---------------------------------------------------#
# 训练时进行数据的随机增强
# 验证时不进行数据的随机增强
#---------------------------------------------------#
image, box = get_random_data(annotation_lines[i],
input_shape,
random=random)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def data_generator_rand(annotation_lines, batch_size, input_shape, anchors,
num_classes):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i],
input_shape,
random=True)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
l_true = [
np.zeros(shape=(batch_size, 416 // {
0: 32,
1: 16,
2: 8
}[l], 416 // {
0: 32,
1: 16,
2: 8
}[l], 9 // 3, 20 + 5)) for l in range(3)
]
yield [image_data, *y_true, *l_true], np.zeros(batch_size)
def data_generator(annotation_lines, batch_size, input_shape, anchors,
num_classes):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
# with Pool(batch_size) as p:
# res = np.array(p.map(get_random_data, annotation_lines[i:i+batch_size]))
# image_data = np.array([r for r in res[:,0]])
# box_data = np.array([r for r in res[:,1]])
# p.close()
# p.join()
# res = np.array([get_random_data(l) for l in lines[:8]])
# box_data = np.array([r for r in res[:,1]])
# image_data = np.array([r for r in res[:,0]])
#print(x.shape, y.shape)
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i],
input_shape,
random=True)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
i = (i + batch_size) % n
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def generate_arrays_from_file(lines, batch_size, train):
# 获取总长度
n = len(lines)
i = 0
while 1:
X_train = [] #300x300x3
Y_train = [] #label
# 获取一个batch_size大小的数据
for b in range(batch_size):
if i == 0:
np.random.shuffle(lines)
name = lines[i].split(';')[0]
label_name = (lines[i].split(';')[1]).strip('\n')
file_name = str(Name.get(int(label_name)))
# 从文件中读取图像
img = Image.open(r".\data\face" + "\\" + file_name + "\\" + name)
if train == True:
img = np.array(get_random_data(img, [image_h, image_w]),
dtype=np.float64)
else:
img = np.array(letterbox_image(img, [image_h, image_w]),
dtype=np.float64)
X_train.append(img)
Y_train.append(label_name)
# 读完一个周期后重新开始
i = (i + 1) % n
# 处理图像
X_train = preprocess_input(
np.array(X_train).reshape(-1, image_h, image_w, 3))
#one-hot
Y_train = np_utils.to_categorical(np.array(Y_train),
num_classes=num_class)
yield (X_train, Y_train)
def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
'''data generator for fit_generator
anchors = {ndarray: (9, 2)} 已经设置好的尺寸
annotation_lines = {list: 3} xml文件的个数，每一行有真实框的坐标和对应物体的类别
batch_size = {int} 1
input_shape = {tuple: 2} (416, 416)
num_classes = {int} 20
把图片和真实框按批转化为数组，根据真实框和锚框得到标签y_true
'''
n = len(annotation_lines) # n = 3
i = 0 # 计数器
while True:
# 1 每batch_size的图片和真实框标签
image_data = [] # 储存图片的列表 [416,416,3]
box_data = [] # 储存框的列表[1,num_gt,5]
for b in range(batch_size):
if i==0:
np.random.shuffle(annotation_lines) #
image, box = get_random_data(annotation_lines[i], input_shape, random=True) # box.shape = (20,5)
image_data.append(image) # imge.shape =【416，416，3】
box_data.append(box)
i = (i+1) % n
# 2 图片和真实框的数据转化为数值
image_data = np.array(image_data) # image_data.shape=[1,416,416,3] 把列表转化为数组，维度会扩展一维
box_data = np.array(box_data) # box.shape=[20,5] (box_data).shape= [1,20,5]
# 3 根据真实框和锚框的宽高获得标签y_true
y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) # box_data(1,20,5),input_shape(416,416), anchors(9,2),num_classes=20
yield [image_data, *y_true], np.zeros(batch_size) # y_true.list=3 [m,h,w,k,25]
def data_generator(annotation_lines, batch_size, input_shape, anchors,
num_classes): #fit_generator，用于训练时向模型输入数据
'''data generator for fit_generator'''
n = len(annotation_lines) #读取样本数
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size): #取batch_size次数据
if i == 0: #i==0时意味着所有样本已经都被使用过了，需要重新开始新一轮读取
np.random.shuffle(annotation_lines) #将样本顺序打混
image, box = get_random_data(
annotation_lines[i], input_shape,
random=True) #读取数据并进行数据增强,image-->(416,416,3),box-->(20,5)
image_data.append(image) #向image_data内添加处理过的image
box_data.append(box) #向box_data内添加处理过的box
i = (i + 1) % n #判定的核心程序，当i+1==n时，即所有样本都读取过了，(i+1) % n则=0
image_data = np.array(image_data) #将image_data转化为numpy格式
box_data = np.array(box_data) #将box_data转化为numpy格式
y_true = preprocess_true_boxes(
box_data, input_shape, anchors,
num_classes) #通过box_data和anchors计算y_true，#将真实坐标转化为yolo需要输入的坐标
yield [image_data, *y_true], np.zeros(
batch_size
) #[image_data, *y_true]为真正的输入项，np.zeros(batch_size)为占位置的向量，不参与计算
def data_generator(annotation_lines, batch_size, input_shape, anchors,
num_classes, teacher):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
#print("data"+str(i) )
image_data = []
box_data = []
for b in range(batch_size):
#if i==0:
# np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i],
input_shape,
random=False)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
m_true = teacher.predict(image_data)
#l_true = [ np.zeros( shape=( batch_size ,416//{0:32, 1:16, 2:8}[l], 416//{0:32, 1:16, 2:8}[l], 9//3, 20+5) ) for l in range(3) ]
if len(m_true) == 3:
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
elif len(m_true) == 2:
anchor_mask = [[3, 4, 5], [0, 1, 2]]
else:
anchor_mask = [[0, 1, 2]]
for l in range(len(m_true)):
pred_xy, pred_wh, pred_conf, pred_class = numpy_yolo_head(
m_true[l], anchors[anchor_mask[l]], input_shape)
pred_detect = np.concatenate(
[pred_xy, pred_wh, pred_conf, pred_class], axis=-1)
#print("inside")
box = np.where(y_true[l][..., 4] > 0.5)
box = np.transpose(box)
for k in range(len(box)):
m_true[l][tuple(box[k])] = pred_detect[tuple(box[k])]
yield image_data, y_true, m_true
def data_generator(annotation_lines,
batch_size,
input_shape,
anchors,
num_classes):
"""data generator for fit_generator
the assignment strategy:
one gt ---> one anchor
1.find which anchor(9 anchors) gt belongs to
2.find which grid gt belongs to
Args:
annotation_lines: a list [anno1, anno2, ...]
batch_size: batch size
input_shape: resolution [h, w]
anchors: anchor boxes
num_classes: the number of class
max_boxes: box_data: [max_boxes, 5]
when have a lot of gt to predict, need to set max_boxes bigger.
Returns:
batch data: [image_data, *y_true], np.zeros(batch_size)
"""
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
# shuffle dataset at begin of epoch
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i], input_shape)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
# get true_boxes
# y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
y_true = preprocess_true_boxes_iou_thres(box_data, input_shape, anchors, num_classes,
iou_threshold=CONFIG.TRAIN.IOU_THRESHOLD)
# use yield to get generator
yield [image_data, *y_true], np.zeros(batch_size)
def data_generator(annotation_lines, batch_size, input_shape, anchors,
num_classes):
'''data generator for fit_generator
Parameters
----------
annotation_lines: list of str, shape=(m, T, 5)
Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.
batch_size: integer,
input_shape: tuple, array-like type, hw, multiples of 32
anchors: ndarray, shape=(N, 2), N行2列（wh），N表示先验框个数, the default N is 9
num_classes: integer,
Returns
-------
[image_data, *y_true]: 矩阵，数组拼接，
image_data: array, shape like image(445, 445)
*y_true: list of array, shape like yolo_outputs, xywh are reletive value
np.zeros(batch_size): array,
'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i == 0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i],
input_shape,
random=True)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors,
num_classes)
# 迭代生成器，迭代一次返回yield后面的值
yield [image_data, *y_true], np.zeros(batch_size)
def get_batch(self, datas):
image_data = []
box_data = []
word_data = []
seg_data = []
for data in datas:
image, box, word_vec, seg_map = get_random_data(
data,
self.input_shape,
self.embed,
self.config,
train_mode=self.train_mode)
word_data.append(word_vec)
image_data.append(image)
box_data.append(box)
seg_data.append(seg_map)
image_data = np.array(image_data)
box_data = np.array(box_data)
word_data = np.array(word_data)
seg_data = np.array(seg_data)
det_data = preprocess_true_boxes(box_data, self.input_shape,
self.anchors)
return image_data, word_data, det_data, seg_data
def data_generator(annotation_lines, batch_size, input_shape, num_classes,
maxDis):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
# 每次开头打乱数据
if i == 0:
np.random.shuffle(annotation_lines)
# 获取一副图片及其中的box
image, box = get_random_data(annotation_lines[i],
input_shape,
max_dis=maxDis,
max_boxes=5)
image_data.append(image)
box_data.append(box)
i = (i + 1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = prepare_ytrue(box_data, input_shape, num_classes)
yield [image_data, y_true], np.zeros(batch_size)
def evaluate(self, tag='image', is_save_images=False):
self.boxes, self.scores, self.eval_inputs = yolo_eval_v2(
self.model.output_shape[0],
self.anchors,
self.input_image_shape,
score_threshold=0.,
iou_threshold=0.)
# Add the class predict temp dict
# pred_tmp = []
groud_truth = [] # wait
seg_prec_all = dict()
id = 0
seg_iou_all = 0.
detect_prec_all = 0.
fd_ts_count = 0.
td_fs_count = 0.
fd_fs_count = 0.
# Predict!!!
test_batch_size = self.batch_size
for start in progressbar.progressbar(range(0, len(self.val_data),
test_batch_size),
prefix='evaluation: '):
end = start + test_batch_size
batch_data = self.val_data[start:end]
images = []
images_org = []
files_id = []
word_vecs = []
sentences = []
gt_boxes = []
gt_segs = []
for data in batch_data:
image_data, box, word_vec, image, sentence, seg_map = get_random_data(
data,
self.input_shape,
self.word_embed,
self.config,
train_mode=False) # box is [1,5]
sentences.extend(sentence)
word_vecs.extend(word_vec)
# evaluate each sentence corresponding to the same image
for ___ in range(len(sentence)):
# groud_truth.append(box[0, 0:4])
gt_boxes.append(box[0, 0:4])
images.append(image_data)
images_org.append(image)
files_id.append(id)
gt_segs.append(seg_map)
id += 1
images = np.array(images)
word_vecs = np.array(word_vecs)
out_bboxes_1, pred_segs, _ = self.model.predict_on_batch(
[images, word_vecs])
pred_segs = self.sigmoid_(pred_segs) # logit to sigmoid
for i, out in enumerate(out_bboxes_1):
# Predict
out_boxes, out_scores = self.sess.run( # out_boxes is [1,4] out_scores is [1,1]
[self.boxes, self.scores],
feed_dict={
# self.eval_inputs: out
self.eval_inputs[0]: np.expand_dims(out, 0),
self.input_image_shape: np.array(self.input_shape),
K.learning_phase(): 0
})
ih = gt_segs[i].shape[0]
iw = gt_segs[i].shape[1]
w, h = self.input_shape
scale = min(w / iw, h / ih)
nw = int(iw * scale)
nh = int(ih * scale)
dx = (w - nw) // 2
dy = (h - nh) // 2
# up sample
pred_seg = cv2.resize(pred_segs[i], self.input_shape)
#nls
if self.use_nls:
pred_seg = self.nls(
pred_seg,
self.box_value_fix(out_boxes[0], self.input_shape),
out_scores[0])
#scale to the size of ground-truth
pred_seg = pred_seg[dy:nh + dy, dx:nw + dx, ...]
pred_seg = cv2.resize(
pred_seg, (gt_segs[i].shape[1], gt_segs[i].shape[0]))
pred_seg = np.reshape(
pred_seg, [pred_seg.shape[0], pred_seg.shape[1], 1])
# segmentation eval
seg_iou, seg_prec = self.cal_seg_iou(gt_segs[i], pred_seg,
self.seg_min_overlap)
seg_iou_all += seg_iou
for item in seg_prec:
if seg_prec_all.get(item):
seg_prec_all[item] += seg_prec[item]
else:
seg_prec_all[item] = seg_prec[item]
# detection eval
pred_box = self.box_value_fix(out_boxes[0], self.input_shape)
score = out_scores[0]
detect_prec = self.cal_detect_iou(pred_box, gt_boxes[i],
self.det_acc_thresh)
detect_prec_all += detect_prec
# caulate IE metric
if detect_prec - seg_prec[0.5] != 0.:
if detect_prec > seg_prec[0.5]:
td_fs_count += 1.
else:
fd_ts_count += 1.
elif detect_prec + seg_prec[0.5] == 0.:
fd_fs_count += 1.
#visualization
if is_save_images and (files_id[i]
in self.eval_save_images_id):
left, top, right, bottom = pred_box
# Draw image
gt_left, gt_top, gt_right, gt_bottom = (
gt_boxes[i]).astype('int32')
image = np.array(images[i] * 255.).astype(np.uint8)
# segement image for saving
seg_image = np.array(
cv2.resize(
np.array(
pred_segs[i] > self.seg_min_overlap).astype(
np.float32), self.input_shape)).astype(
np.uint8) * 255
label = '{:%.2f}' % score
color = self.colors[0]
cv2.rectangle(image, (left, top), (right, bottom), color,
2)
cv2.rectangle(image, (gt_left, gt_top),
(gt_right, gt_bottom), self.colors[1], 2)
font_size = 0.8
cv2.putText(image, label, (left, max(top - 3, 0)),
cv2.FONT_HERSHEY_SIMPLEX, font_size, color, 2)
cv2.putText(image, 'ground_truth',
(gt_left, max(gt_top - 3, 0)),
cv2.FONT_HERSHEY_SIMPLEX, font_size,
self.colors[1], 2)
cv2.putText(image, str(sentences[i]), (20, 20),
cv2.FONT_HERSHEY_SIMPLEX, .9, self.colors[2],
2)
cv2.imwrite('./images/' + str(files_id[i]) + '.jpg', image)
log_images(self.tensorboard, tag + '/' + str(files_id[i]),
[image], 0)
log_images(self.tensorboard,
tag + '/' + str(files_id[i]) + '_seg',
[seg_image], 0)
miou_seg = seg_iou_all / id
miou_detect = detect_prec_all / id
ie_score = (td_fs_count + fd_ts_count) / id
for item in seg_prec_all:
seg_prec_all[item] /= id
return miou_detect, miou_seg, seg_prec_all, ie_score
def evaluate(self, tag='image', is_save_images=False):
self.boxes, self.scores, self.eval_inputs = yolo_eval_v2(self.model.output_shape[0],self.anchors, self.input_image_shape,
score_threshold=0., iou_threshold=0.)
# Add the class predict temp dict
# pred_tmp = []
groud_truth = [] # wait
seg_prec_all = dict()
id =0
seg_iou_all =0.
detect_prec_all = 0.
fd_ts_count=0.
td_fs_count=0.
fd_fs_count=0.
# Predict!!!
test_batch_size =self.batch_size
for start in progressbar.progressbar(range(0, len(self.val_data), test_batch_size), prefix='evaluation: '):
end = start +test_batch_size
batch_data = self.val_data[start:end]
images = []
images_org = []
files_id = []
word_vecs = []
sentences = []
gt_boxes = []
gt_segs = []
for data in batch_data:
image_data, box, word_vec, image, sentence, seg_map = get_random_data(data, self.input_shape,
self.word_embed, self.config,
train_mode=False) # box is [1,5]
sentences.extend(sentence)
word_vecs.extend(word_vec)
# evaluate each sentence corresponding to the same image
for ___ in range(len(sentence)):
# groud_truth.append(box[0, 0:4])
gt_boxes.append(box[0, 0:4])
images.append(image_data)
images_org.append(image)
files_id.append(id)
gt_segs.append(seg_map)
id += 1
images = np.array(images)
word_vecs = np.array(word_vecs)
out_bboxes_1,_ = self.model.predict_on_batch([images, word_vecs])
for i, out in enumerate(out_bboxes_1):
# Predict
out_boxes, out_scores = self.sess.run( # out_boxes is [1,4] out_scores is [1,1]
[self.boxes, self.scores],
feed_dict={
# self.eval_inputs: out
self.eval_inputs[0]: np.expand_dims(out, 0),
self.input_image_shape: np.array(self.input_shape),
K.learning_phase(): 0
})
ih = gt_segs[i].shape[0]
iw = gt_segs[i].shape[1]
w, h = self.input_shape
scale = min(w / iw, h / ih)
nw = int(iw * scale)
nh = int(ih * scale)
dx = (w - nw) // 2
dy = (h - nh) // 2
# detection eval
pred_box = self.box_value_fix(out_boxes[0],self.input_shape)
score = out_scores[0]
detect_prec = self.cal_detect_iou(pred_box, gt_boxes[i], self.det_acc_thresh)
detect_prec_all += detect_prec
#visualization
if is_save_images and (files_id[i] in self.eval_save_images_id):
left, top, right, bottom = pred_box
# Draw image
gt_left, gt_top, gt_right, gt_bottom = (gt_boxes[i]).astype('int32')
image = np.array(images[i] * 255.).astype(np.uint8)
label = '{:%.2f}' % score
color = self.colors[0]
cv2.rectangle(image, (left, top), (right, bottom), color, 2)
cv2.rectangle(image, (gt_left, gt_top), (gt_right, gt_bottom), self.colors[1], 2)
font_size = 0.8
cv2.putText(image,
label,
(left, max(top - 3, 0)),
cv2.FONT_HERSHEY_SIMPLEX,
font_size, color, 2)
cv2.putText(image,
'ground_truth',
(gt_left, max(gt_top - 3, 0)),
cv2.FONT_HERSHEY_SIMPLEX,
font_size, self.colors[1], 2)
cv2.putText(image,
str(sentences[i]),
(20, 20),
cv2.FONT_HERSHEY_SIMPLEX,
.9, self.colors[2], 2)
cv2.imwrite('./images/'+str(files_id[i])+'.jpg',image)
log_images(self.tensorboard, tag + '/' + str(files_id[i]), [image], 0)
miou_detect = detect_prec_all / id
return miou_detect
from utils.utils import get_random_data, prepare_ytrue
import numpy as np
import matplotlib.pyplot as plt
input_shape = (416, 416)
anno1 = "./VOCdevkit/GBG2021/JPEGImages/can(21).jpg 169,129,272,3.13,7 170,263,255,3.12,7"
anno2 = "./VOC/JPEGImages/grasp_garbage00003.jpg 253,344,153,3.021,1 455,466,152,0.02,1"
# get_random_data(anno, input_shape)
box_data = []
box_data.append(get_random_data(anno1, (416, 416), max_boxes=3)[1])
#box_data.append(get_random_data(anno2, (416, 416), max_boxes=3)[1])
box_data = np.array(box_data)
print("box_data.shape : ", box_data.shape)
yt = prepare_ytrue(box_data, input_shape, num_classes=12)
print("yt.shape ： ", yt.shape)
for i in range(6): # yt.shape[-1]
mater = yt[0, :, :, i]
plt.matshow(mater, cmap=plt.cm.Blues)
plt.show()
'''for i in range(yt.shape[-1]):
mater = yt[1, :, :, i]
plt.matshow(mater, cmap=plt.cm.Reds)
plt.show()'''
generated_path = '/home/qkh/hdd1/data/gang_jin/' # set the path for saving generated h5 file
input_shape = (544, 544)
aug_scale = 64
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
all_imgs, _, _ = get_pascal_detection_data(input_path=dataset_path)
dataAug = DataAugmentForObjectDetection()
image_data = []
box_data = []
for i in tqdm(range(len(all_imgs))):
# 获取原始图像
origin_img, origin_box = get_random_data(all_imgs[i],
input_shape,
random=False)
origin_img = exposure.rescale_intensity(origin_img, out_range=(0, 255))
origin_img = np.uint8(origin_img)
image_data.append(origin_img)
box_data.append(origin_box)
# 获取增强数据
# data augmentation
for b in range(aug_scale - 1):
auged_img, auged_box = dataAug.dataAugment(origin_img,
origin_box[:, :4])
auged_img = np.uint8(auged_img)
tmp_box = np.zeros(origin_box.shape)
tmp_box[:, :4] = auged_box[:, :4]
tmp_box[:, 4] = origin_box[:, 4]
