offset_px1 = 0 #(px1 - x1_) / side_len
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
# 剪切下图片，并进行大小缩放
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size))
iou = utils.iou(crop_box, np.array(boxes))[0]
if iou > 0.65: # 正样本
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()
face_resize.save(
os.path.join(positive_image_dir,
"{0}.jpg".format(positive_count)))
positive_count += 1
elif iou > 0.4: # 部分样本
y1_ = np.maximum(0, cy_ - side_len / 2)
x2_ = x1_ + side_len
y2_ = y1_ + side_len
crop_box = np.array([x1_, y1_, x2_, y2_])
# 计算坐标的偏移值
offset_x1 = (x1 - x1_) / side_len
offset_y1 = (y1 - y1_) / side_len
offset_x2 = (x2 - x2_) / side_len
offset_y2 = (y2 - y2_) / side_len
# 剪切下图片，并进行大小缩放
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size),
Image.ANTIALIAS)
iou = utils.iou(crop_box, _boxes, False)[0]
if iou > 0.5: # positive and part
face_resize.save(
os.path.join(person_dir, "{0}.jpg".format(count)))
count += 1
elif iou < 0.3: # negative
print("Negative simples, dropout")
except Exception as e:
print("List empty now!")
# traceback.print_exc()
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
#这里readlines会有/n readline没有
# test = open(positive_anno_filename, "r")
# print(test.readline())
# exit()
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
strs = line.split()
#strs = ['000001.jpg', '95', '71', '226', '313']
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 x1 < 0 or y1 < 0 or w < 0 or h < 0:
continue
#原图的框框
boxes = [[x1, y1, x2, y2]]
cx = x1 + w / 2
cy = y1 + h / 2
side_len = max(w, h)
count = 0
for _ in range(5):
# 边长放缩比例
side_len_scale = np.random.uniform(0.8, 1.2)
_side_len = int(side_len * side_len_scale)
if _side_len == 0:
continue
# print(_side_len)
if np.random.randint(0, 10) < 1:
#生成同一范围内两个随机数
#给xy不同比例的偏移量
positive_scale = np.random.uniform(0, 0.08, 2)
# part_scale = np.random.uniform(0.15, 0.25)
part_scale = np.random.uniform(0.2, 0.6, 2)
part_scale = part_scale if np.random.randint(
0, 2) else -part_scale
offset_scale = [positive_scale, part_scale]
seed = offset_scale[np.random.randint(0, 2)]
_cx = cx + int(-_side_len * seed[0])
_cy = cy + int(-_side_len * seed[1])
_x1 = _cx - _side_len / 2
_y1 = _cy - _side_len / 2
_x2 = _x1 + _side_len
_y2 = _y1 + _side_len
else:
if img_w - _side_len <= 0 or img_h - _side_len <= 0:
continue
else:
_x1 = np.random.randint(0, img_w - _side_len)
_y1 = np.random.randint(0, img_h - _side_len)
_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
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
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]
if iou > 0.65:
if positive_count >= stop_value * 0.2:
continue
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))
#flush 写入一次保存一次
positive_anno_file.flush()
face_resize.save(
os.path.join(positive_image_dir,
"{0}.jpg".format(positive_count)))
positive_count += 1
elif 0.5 > iou > 0.25:
if part_count >= stop_value * 0.2:
continue
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 < 0.1:
if negative_count >= stop_value * 0.6:
continue
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
print(positive_count, part_count, negative_count)
if count >= stop_value:
break
finally:
positive_anno_file.close()
negative_anno_file.close()
part_anno_file.close()
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
# 剪切下图片，并进行大小缩放
face_crop = img.crop(crop_box) # 抠图
face_resize = face_crop.resize(
(face_size, face_size),
Image.ANTIALIAS) #按照人脸尺寸进行缩放：12/24/48
iou = utils.iou(crop_box,
np.array(boxes))[0] # 抠出来的框和原来的框计算IOU
if iou > 0.6: # 正样本；原为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() # flush：将缓存区的数据写入文件
face_resize.save(
os.path.join(
positive_image_dir,
"{0}.jpg".format(positive_count))) # 保存
positive_count += 1
def generatedataset(pass_used=False, treat_img=True, gen_label=True):
pic_used_dict = {12: "12.txt", 24: "24.txt", 48: "48.txt"}
# 新旧标签列表
datalist_read = []
# datalist_write = []
with open(labelfile, 'r') as f:
for i, line in enumerate(f.readlines()):
# 第一行为标量行，不读
if (i > 0):
datalist_read.append(line.strip())
ioulist = []
# 定义三个样本的计数
positive_num_ = 0
negative_num_ = 0
part_num_ = 0
positive_num = 0
negative_num = 0
part_num = 0
for face_size in face_sizes:
datalist_write = [] # 注意datalist清空位置，注意计数
'''
将已生成的图片存起来，下次不再生成'''
pic_used_txt = pic_used_dict.get(face_size)
if pass_used:
pic_used_list = []
if os.path.exists(pic_used_txt):
with open(pic_used_txt) as f:
for line in f.readlines():
if line[0].isdigit():
pic_used_list.append(line.strip().split()[0])
elif os.path.exists(pic_used_txt):
os.remove(pic_used_txt)
"定义三个尺寸的文件保存路径"
# positive_savepath = os.path.join(pic_savepath, str(face_size), "positive")
# negative_savepath = os.path.join(pic_savepath, str(face_size), "negative")
# part_savepath = os.path.join(pic_savepath, str(face_size), "part")
# pic_savepathlist = [negative_savepath,positive_savepath,part_savepath]
# 定义图片保存文件夹
# pic_savedict = {
# 0: os.path.join(pic_savepath, str(face_size), "negative"),
# 1: os.path.join(pic_savepath, str(face_size), "positive"),
# 2: os.path.join(pic_savepath, str(face_size), "part")
# }
'''
定义每个尺寸图片保存文件夹和标签保存文件的dict
图片要按照置信分类，标签不用'''
save_dict = {
"pic": {
0: os.path.join(pic_savepath, str(face_size), "negative"),
1: os.path.join(pic_savepath, str(face_size), "positive"),
2: os.path.join(pic_savepath, str(face_size), "part")
},
"label":
os.path.join(label_savepath,
"label_{0}.txt".format(str(face_size)))
}
# positive part negative集中放置，这几个文件夹不用生成
# for i in [0, 1, 2]:
# utils.makedir(save_dict['pic'][i])
"遍历标签数据"
for i, strdata in enumerate(datalist_read):
"分隔每行数据"
data = strdata.strip().split()
if pass_used:
if data[0] in pic_used_list:
continue
# x,y,w,h = map(int,data[1],data[2],data[3],data[4])#这个写法不行
# x1, y1, w, h, width, height = map(int, data[1:7])#本身可以实现数据的strip()
# x1, y1, w, h, width, height = map(int, map(str.strip,data[1:7]))#不以逗号分隔了，改为空格
x1, y1, x2, y2, w, h, width, height = map(int, data[1:9])
"图片过滤"
# if (x1 < 0 or y1 < 0 or w < 0 or h < 0 or max(w, h) < 40):
# continue
"判断是否有小于0的元素，框的大小是否比40小，框是否在图片内"
if any([d < 0 for d in map(int, data[1:9])
]) or min(w, h) < 40 or max(w, h) > min(width, height):
# print("false data: ", data)
# 输出错误数据
continue
'''
补方框'''
# "判断框是否为近方框"
# if w / (w + h) > 0.6 or w / (w + h) < 0.3:
# pass
# else:
# continue
# x1, y1, x2, y2, w, h, width, height = map(int, data[1:9])
"计算右下角坐标x1,y1和中心点x0,y0"
# x2, y2 = x1 + w, y1 + h
# x0, y0 = x1 + w // 2, y1 + h // 2
for j in range(len(wh_scales)):
k = 0
while k < each_pic_num:
'''
name是加载图片的名称
pic_name 是新生成图片的名称'''
name, offset, box, box_ = utils.getoffset(
data, wh_scale=wh_scales[j], size_scale=size_scale)
iou_value = utils.iou(box, box_)
# 生成图片文件名
pic_name = "%s_%d_0%1d%02d.jpg" % (
name.split('.')[0], face_size, j, k
) # 文件名中第二个"_"后的第一个0代表正或部分样本，另外生成的负样本此处为1
# 生成图片路径
"置信"
confidence = getconfidence(iou_value)
"图片保存路径"
if confidence == -1:
continue
else:
ioulist.append(iou_value)
k += 1
pic_savedir = os.path.join(pic_savepath, str(face_size))
utils.makedir(pic_savedir)
pic_savefile = os.path.join(pic_savedir, pic_name)
"标签保存路径"
label_savefile = os.path.join(save_dict['label'])
"原图片路径"
pic_file = os.path.join(picpath, name)
if treat_img:
utils.imgTreat(pic_file, pic_savefile, box_, face_size)
datalist_write.append(
utils.generateLabeldata(pic_name, confidence, offset))
# 三个样本计数
if positive_range[0] < iou_value < positive_range[1]:
positive_num_ += 1
elif part_range[0] < iou_value < part_range[1]:
part_num_ += 1
elif negative_range[0] < iou_value < negative_range[1]:
negative_num_ += 1
if i % 100 == 0:
total_num_ = positive_num_ + part_num_ + negative_num_
print("epoch", i, positive_num_, part_num_, negative_num_,
total_num_)
if all([
positive_num_ > 0, part_num_ > 0,
negative_num_ > 0, total_num_ > 0
]):
print("positive: %.3f, %.3f" %
(positive_num_ / part_num_,
positive_num_ / negative_num_))
print("ratio: %.3f, %.3f, %.3f" %
(positive_num_ / total_num_,
part_num_ / total_num_,
negative_num_ / total_num_)) # 临时查看一下数量
print("********************")
if pass_used:
with open(pic_used_txt, 'a') as pic_used_file:
print(data[0], file=pic_used_file)
if len(datalist_write) != 0 and gen_label:
utils.generateLabel(datalist_write, label_savefile, face_size)
# 三个样本计数
for i in ioulist:
if positive_range[0] < i < positive_range[1]:
positive_num += 1
elif part_range[0] < i < part_range[1]:
part_num += 1
elif negative_range[0] < i < negative_range[1]:
negative_num += 1
total_num = positive_num + part_num + negative_num
print(positive_num, part_num, negative_num, total_num)
y2_ = y1_ + side_len
crop_box = np.array([x1_, y1_, x2_, y2_])
# 计算坐标的偏移值
offset_x1 = (x1 - x1_) / side_len
offset_y1 = (y1 - y1_) / side_len
offset_x2 = (x2 - x2_) / side_len
offset_y2 = (y2 - y2_) / side_len
# 剪切下图片，并进行大小缩放
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size),
Image.ANTIALIAS)
iou = utils.iou(crop_box, _boxes, False)[0]
iou_min = utils.iou(crop_box, _boxes, True)[0]
if iou > 0.65 and iou_min > 0.65: # 正样本
positive_file.write(
"positive/{0}.jpg {1} {2} {3} {4} {5}\n".
format(positive_count, 1, offset_x1, offset_y1,
offset_x2, offset_y2))
positive_file.flush()
face_resize.save(
os.path.join(positive_image_dir,
"{0}.jpg".format(positive_count)))
positive_count += 1
elif iou > 0.4 and iou_min > 0.4: # 部分样本
part_file.write(
offset_px1 = 0#(px1 - x1_) / side_len # 人的五官特征的偏移值
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
# 剪切下图片，并进行大小缩放
face_crop = img.crop(crop_box) # 抠图
face_resize = face_crop.resize((face_size, face_size),Image.ANTIALIAS) #按照人脸尺寸进行缩放：12/24/48
iou = utils.iou(crop_box, np.array(boxes))[0] # 抠出来的框和原来的框计算IOU
if iou > 0.6: # 正样本；原为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() # flush：将缓存区的数据写入文件
face_resize.save(os.path.join(positive_image_dir, "{0}.jpg".format(positive_count))) # 保存
positive_count += 1
elif iou > 0.4: # 部分样本；原为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)) # 写入txt文件
def gen_sample(save_path, face_size, stop_value):
print("gen size:{} image".format(face_size))
# 样本图片存储路径
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")
# 造出三种路径下的9个文件夹，// 12,24,48
for dir_path in [positive_image_dir, negative_image_dir, part_image_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# 样本标签存储路径
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")
# 样本的统计数
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:
# 切割非空值元素
strs = line.split()
# print(strs)
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())
# 如果坐标或边长为负数，或者人脸框面积和最大边长正方形面积比(IOU)低于0.7(无法生成正样本)，则丢弃
if x1 < 0 or y1 < 0 or w < 0 or h < 0 or (w * h) / (max(w, h) * max(w, h)) <= 0.7:
# if x1 < 0 or y1 < 0 or w < 0 or h < 0 or (min(w,h)*min(w,h)) / (w*h) <= 0.7:
continue
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())
boxes = [[x1, y1, x2, y2]]
# 计算出人脸中心点位置
cx = x1 + w / 2
cy = y1 + h / 2
side_len = random.choice([w, h]) # 随机选一个边，（正方形）
# 一张图片生成的样本统计数
single_img_pos = 0
single_img_part = 0
single_img_neg = 0
while True:
if single_img_pos < 3:
_side_len = side_len + side_len * random.uniform(-0.2, 0.2) + 1
_cx = cx + cx * random.uniform(-0.2, 0.2) + 1
_cy = cy + cy * random.uniform(-0.2, 0.2) + 1
elif single_img_part < 3:
_side_len = side_len + side_len * random.uniform(-1, 1) + 1
_cx = cx + cx * random.uniform(-1, 1) + 1
_cy = cy + cy * random.uniform(-1, 1) + 1
elif single_img_neg < 9:
_side_len = side_len + side_len * random.uniform(-2, 2) + 1
_cx = cx + cx * random.uniform(-2, 2) + 1
_cy = cy + cy * random.uniform(-2, 2) + 1
_x1 = _cx - _side_len / 2 # 偏移后的中心点换算回偏移后起始点X,Y
_y1 = _cy - _side_len / 2
_x2 = _x1 + _side_len # 获得偏移后的X2,Y2
_y2 = _y1 + _side_len
# 偏移后的的坐标点对应的是正方形
# 判断偏移超出整张图片的就跳过,不截图
if _x1 < 0 or _y1 < 0 or _x2 > img_w or _y2 > img_h or _side_len < face_size:
continue
offset_x1 = (x1 - _x1) / _side_len # 获得换算后的偏移率
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
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
# 剪切下图片，并进行大小缩放
crop_box = [_x1, _y1, _x2, _y2] # 获得需要截取图片样本的坐标
face_crop = img.crop(crop_box)
face_resize = face_crop.resize((face_size, face_size))
iou = utils.iou(crop_box, np.array(boxes))[0]
if iou > 0.7 and single_img_pos < 3: # 正样本
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()
face_resize.save(os.path.join(positive_image_dir, "{0}.jpg".format(positive_count)))
single_img_pos += 1
positive_count += 1
elif 0.7 > iou > 0.3 and single_img_part < 3: # 部分样本
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)))
single_img_part += 1
part_count += 1
elif iou < 0.1 and single_img_neg < 9: # 负样本
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)))
single_img_neg += 1
negative_count += 1
# 统计每张图片被生成各类样本的次数,当一张原始图像按照3:3:9的比例生成的数量达到15张时，换下一张图像
if single_img_pos + single_img_part + single_img_neg >= 15:
break
# 统计总样本数
count = positive_count + part_count + negative_count
# 判断总样本数据是否到要求
if count >= stop_value:
break
except:
traceback.print_exc()
except:
traceback.print_exc()