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Create_sample2.py
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Create_sample2.py
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import os
from PIL import Image
import numpy as np
import utils
import traceback
anno_src = r"D:\CelebA\list_bbox_celeba.txt"
img_dir = r"D:\CelebA\img_celeba"
save_path = r"D:\CelebA_40w"
def gen_sample(face_size, stop_value):
print("gen size:{} image".format(face_size))
positive_image_dir = os.path.join(save_path, str(face_size), "positive")
negative_image_dir = os.path.join(save_path, str(face_size), "negative")
part_image_dir = os.path.join(save_path, str(face_size), "part")
for dir_path in [positive_image_dir, negative_image_dir, part_image_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
positive_anno_filename = os.path.join(save_path, str(face_size), "positive.txt")
negative_anno_filename = os.path.join(save_path, str(face_size), "negative.txt")
part_anno_filename = os.path.join(save_path, str(face_size), "part.txt")
positive_count = 0
negative_count = 0
part_count = 0
try:
positive_anno_file = open(positive_anno_filename, "w")
negative_anno_file = open(negative_anno_filename, "w")
part_anno_file = open(part_anno_filename, "w")
for i, line in enumerate(open(anno_src)):
if i < 2:
continue
try: # 尝试下面程序，有错误就抛出错误。
strs = line.split()
image_filename = strs[0].strip()
print(image_filename)
image_file = os.path.join(img_dir, image_filename)
with Image.open(image_file) as img:
img_w, img_h = img.size
x1 = float(strs[1].strip())
y1 = float(strs[2].strip())
w = float(strs[3].strip())
h = float(strs[4].strip())
x2 = float(x1 + w)
y2 = float(y1 + h)
px1 = 0 # float(strs[5].strip())
py1 = 0 # float(strs[6].strip())
px2 = 0 # float(strs[7].strip())
py2 = 0 # float(strs[8].strip())
px3 = 0 # float(strs[9].strip())
py3 = 0 # float(strs[10].strip())
px4 = 0 # float(strs[11].strip())
py4 = 0 # float(strs[12].strip())
px5 = 0 # float(strs[13].strip())
py5 = 0 # float(strs[14].strip())
if max(w, h) < 40 or x1 < 0 or y1 < 0 or w < 0 or h < 0:
continue
boxes = [[x1, y1, x2, y2]] # 加两个框的目的是方便做IOU
cx = x1 + w / 2
cy = y1 + h / 2
# float_num = [0.1, 0.5, 0.5, 0.5, 0.9, 0.9, 0.9, 0.9, 0.9]
side_len = max(w, h)
float_num = [0.1, 0.5, 0.5, 0.5, 0.9, 0.9, 0.9, 0.9, 0.9]
seed = float_num[np.random.randint(0, len(float_num))] # 0.1-0.9
count = 0
for _ in range(5):
_side_len = side_len + np.random.randint(int(-side_len * seed), int(side_len * seed))
_cx = cx + np.random.randint(int(-cx * seed), int(cx * seed)) # 让中心点偏移
_cy = cy + np.random.randint(int(-cy * seed), int(cy * seed))
_x1 = _cx - _side_len / 2
_y1 = _cy - _side_len / 2
_x2 = _x1 + _side_len
_y2 = _y1 + _side_len
if _x1 < 0 or _y1 < 0 or _x2 > img_w or _y2 > img_h:
continue
offset_x1 = (x1 - _x1) / _side_len # 真实框x坐标减去偏移框的坐标再除以偏移框的边长
offset_y1 = (y1 - _y1) / _side_len
offset_x2 = (x2 - _x2) / _side_len
offset_y2 = (y2 - _y2) / _side_len
offset_px1 = 0 # (px1 - _x1) / side_len # 五个关键点都只对一个点进行偏移
offset_py1 = 0 # (py1 - y1_) / side_len
offset_px2 = 0 # (px2 - x1_) / side_len
offset_py2 = 0 # (py2 - y1_) / side_len
offset_px3 = 0 # (px3 - x1_) / side_len
offset_py3 = 0 # (py3 - y1_) / side_len
offset_px4 = 0 # (px4 - x1_) / side_len
offset_py4 = 0 # (py4 - y1_) / side_len
offset_px5 = 0 # (px5 - x1_) / side_len
offset_py5 = 0 # (py5 - y1_) / side_len
crop_box = [_x1, _y1, _x2, _y2]
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size))
iou = utils.iou(crop_box, np.array(boxes))[0] # 取出标量
# print(utils.iou(crop_box, np.array(boxes))) # [0.73625804]
# print(iou) # 0.7362580437210052
if iou > 0.65:
positive_anno_file.write(
"positive/{0}.jpg {1} {2} {3} {4} {5} {6} {7} {8} {9} {10} {11} {12} {13} {14} {15}\n".format(
positive_count, 1, offset_x1, offset_y1,
offset_x2, offset_y2, offset_px1, offset_py1, offset_px2, offset_py2, offset_px3,
offset_py3, offset_px4, offset_py4, offset_px5, offset_py5))
positive_anno_file.flush() # 释放内存
face_resize.save(os.path.join(positive_image_dir, "{0}.jpg".format(positive_count)))
positive_count += 1
elif 0.6 > iou > 0.4:
part_anno_file.write(
"part/{0}.jpg {1} {2} {3} {4} {5} {6} {7} {8} {9} {10} {11} {12} {13} {14} {15}\n".format(
part_count, 2, offset_x1, offset_y1, offset_x2,
offset_y2, offset_px1, offset_py1, offset_px2, offset_py2, offset_px3,
offset_py3, offset_px4, offset_py4, offset_px5, offset_py5))
part_anno_file.flush()
face_resize.save(os.path.join(part_image_dir, "{0}.jpg".format(part_count)))
part_count += 1
elif iou < 0.05:
negative_anno_file.write(
"negative/{0}.jpg {1} 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n".format(negative_count, 0))
negative_anno_file.flush()
face_resize.save(os.path.join(negative_image_dir, "{0}.jpg".format(negative_count)))
negative_count += 1
_boxes = np.array(boxes)
for i in range(3): # 负样本增强
side_len = np.random.randint(face_size, min(img_w, img_h))
x_ = np.random.randint(0, img_w - 0.9*side_len)
y_ = np.random.randint(0, img_h - 0.9*side_len)
if x_ < 0 or y_ < 0 or (x_ + side_len) > img_w or (y_ + side_len) > img_h:
continue
crop_box = np.array([x_, y_, x_ + side_len, y_ + side_len])
if np.max(utils.iou(crop_box, _boxes)) < 0.01: # 裁剪框和标签框作比较
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size), Image.ANTIALIAS) # 防止图像变形
negative_anno_file.write("negative/{0}.jpg {1} 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n".format(
negative_count, 0))
negative_anno_file.flush()
face_resize.save(os.path.join(negative_image_dir, "{0}.jpg".format(negative_count)))
negative_count += 1
# count = positive_count + part_count + negative_count
# if count >= stop_value:
# break
except:
traceback.print_exc() # 抛出错误类型
finally:
positive_anno_file.close()
negative_anno_file.close()
part_anno_file.close()
# gen_sample(12, 1000000)
# gen_sample(24, 1000000)
gen_sample(48, 1000000)