ny = npr.randint(0, height - size)
crop_box = np.array([nx, ny, nx + size, ny + size])
Iou = IoU(crop_box, boxes)
cropped_im = img[ny:ny + size, nx:nx + size, :]
resized_im = cv2.resize(cropped_im, (stdsize, stdsize),
interpolation=cv2.INTER_LINEAR)
if np.max(Iou) < 0.3:
# Iou with all gts must below 0.3
'''负样本的标签为 0'''
item_id += 1
''' size 12'''
if (lmdb_id == 0):
mtcnn_datum = data_tran_tool.array_to_mtcnndatum(
resized_im, 0, [-1.0, -1.0, -1.0, -1.0])
keystr = '{:0>8d}'.format(item_id)
lmdb_txn_12.put(keystr, mtcnn_datum.SerializeToString())
# write batch
if (item_id) % batch_size == 0:
if (lmdb_id == 0):
lmdb_txn_12.commit()
lmdb_txn_12 = lmdb_env_12.begin(write=True)
n_idx += 1
neg_num += 1
# pos part
for box in boxes:
# box (x_left, y_top, x_right, y_bottom)
x1, y1, x2, y2 = box
w = x2 - x1 + 1
h = y2 - y1 + 1
interpolation=cv2.INTER_LINEAR)
else:
resized_im48 = cv2.resize(cropped_im, (48, 48),
interpolation=cv2.INTER_LINEAR)
box_ = box.reshape(1, -1)
if IoU(crop_box, box_) >= 0.65:
#save_file = os.path.join(pos_save_dir, "%s.jpg"%p_idx)
#cv2.imwrite(save_file, resized_im)
#f1.write(str(stdsize)+"/positive/%s"%p_idx + ' 1 %f %f %f %f\n'%(offset_x1, offset_y1, offset_x2, offset_y2))
'''正样本的标签为 1'''
item_id += 1
''' size 12'''
if (lmdb_id == 0):
mtcnn_datum = data_tran_tool.array_to_mtcnndatum(
resized_im12, 1,
[offset_x1, offset_y1, offset_x2, offset_y2])
keystr = '{:0>8d}'.format(item_id)
lmdb_txn_12.put(keystr,
mtcnn_datum.SerializeToString())
elif (lmdb_id == 1):
mtcnn_datum = data_tran_tool.array_to_mtcnndatum(
resized_im24, 1,
[offset_x1, offset_y1, offset_x2, offset_y2])
keystr = '{:0>8d}'.format(item_id)
lmdb_txn_24.put(keystr,
mtcnn_datum.SerializeToString())
else:
mtcnn_datum = data_tran_tool.array_to_mtcnndatum(
# 打乱样本顺序
random.shuffle(list_item)
for ann in list_item:
line = ann.strip().split(' ')
item_id += 1
# 读入图像
im_path = rootDir + line[0]
img = cv2.imread(im_path)
label = int(line[1])
nx1 = float(line[2])
ny1 = float(line[3])
nx2 = float(line[4])
ny2 = float(line[5])
mtcnn_datum = data_tran_tool.array_to_mtcnndatum(img, label, [nx1, ny1, nx2, ny2])
keystr = '{:0>8d}'.format(item_id)
lmdb_txn_12.put(keystr, mtcnn_datum.SerializeToString())
# write batch
if(item_id) % batch_size == 0:
if(lmdb_id == 0):
lmdb_txn_12.commit()
lmdb_txn_12 = lmdb_env_12.begin(write=True)
if (item_id+1) % batch_size != 0:
if(lmdb_id == 0):
lmdb_txn_12.commit()
lmdb_env_12.close()
print 'last batch'
print "There are %d images in total" % item_id