118 lines
5.9 KiB
Python
118 lines
5.9 KiB
Python
import cv2
|
|
|
|
from app.core.config import PRIORITY_DICT
|
|
|
|
|
|
def organize_body(layer):
|
|
body_layer = dict(priority=0,
|
|
name=layer["name"].lower(),
|
|
image=layer['body_image'],
|
|
image_url=layer['body_path'],
|
|
mask_image=None,
|
|
mask_url=None,
|
|
sacle=1,
|
|
# mask=layer['body_mask'],
|
|
position=(0, 0))
|
|
return body_layer
|
|
|
|
|
|
def organize_clothing(layer):
|
|
# 起始坐标
|
|
start_point = calculate_start_point(layer['keypoint'], layer['scale'], layer['clothes_keypoint'], layer['body_point_test'], layer["offset"], layer["resize_scale"])
|
|
# 前片数据
|
|
front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
|
|
name=f'{layer["name"].lower()}_front',
|
|
image=layer["front_image"],
|
|
# mask_image=layer['front_mask_image'],
|
|
image_url=layer['front_image_url'],
|
|
mask_url=layer['mask_url'],
|
|
sacle=layer['scale'],
|
|
clothes_keypoint=layer['clothes_keypoint'],
|
|
position=start_point,
|
|
resize_scale=layer["resize_scale"],
|
|
mask=cv2.resize(layer['mask'], layer["front_image"].size),
|
|
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
|
|
pattern_image_url=layer['pattern_image_url'],
|
|
pattern_image=layer['pattern_image'],
|
|
# back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
|
|
)
|
|
# 后片数据
|
|
back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
|
|
name=f'{layer["name"].lower()}_back',
|
|
image=layer["back_image"],
|
|
# mask_image=layer['back_mask_image'],
|
|
image_url=layer['back_image_url'],
|
|
mask_url=layer['mask_url'],
|
|
sacle=layer['scale'],
|
|
clothes_keypoint=layer['clothes_keypoint'],
|
|
position=start_point,
|
|
resize_scale=layer["resize_scale"],
|
|
mask=cv2.resize(layer['mask'], layer["front_image"].size),
|
|
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
|
|
pattern_image_url=layer['pattern_image_url'],
|
|
# back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
|
|
)
|
|
return front_layer, back_layer
|
|
|
|
|
|
def organize_accessories(layer):
|
|
# 起始坐标
|
|
start_point = (0, 0)
|
|
# 前片数据
|
|
front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
|
|
name=f'{layer["name"].lower()}_front',
|
|
image=layer["front_image"],
|
|
# mask_image=layer['front_mask_image'],
|
|
image_url=layer['front_image_url'],
|
|
mask_url=layer['mask_url'],
|
|
sacle=layer['scale'],
|
|
clothes_keypoint=(0, 0),
|
|
position=start_point,
|
|
resize_scale=layer["resize_scale"],
|
|
mask=cv2.resize(layer['mask'], layer["front_image"].size),
|
|
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
|
|
pattern_image_url=layer['pattern_image_url'],
|
|
pattern_image=layer['pattern_image'],
|
|
# back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
|
|
)
|
|
# 后片数据
|
|
back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
|
|
name=f'{layer["name"].lower()}_back',
|
|
image=layer["back_image"],
|
|
# mask_image=layer['back_mask_image'],
|
|
image_url=layer['back_image_url'],
|
|
mask_url=layer['mask_url'],
|
|
sacle=layer['scale'],
|
|
clothes_keypoint=(0, 0),
|
|
position=start_point,
|
|
resize_scale=layer["resize_scale"],
|
|
mask=cv2.resize(layer['mask'], layer["front_image"].size),
|
|
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
|
|
pattern_image_url=layer['pattern_image_url'],
|
|
# back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
|
|
)
|
|
return front_layer, back_layer
|
|
|
|
|
|
def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale):
|
|
"""
|
|
Align left
|
|
Args:
|
|
keypoint_type: string, "waistband" | "shoulder" | "ear_point"
|
|
scale: float
|
|
clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]}
|
|
body_point: dict, containing keypoint data of body figure
|
|
|
|
Returns:
|
|
start_point: tuple (x', y')
|
|
x' = y_body - y1 * scale + offset
|
|
y' = x_body - x1 * scale + offset
|
|
|
|
"""
|
|
side_indicator = f'{keypoint_type}_left'
|
|
start_point = (
|
|
int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator][0]) * scale), # y
|
|
int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator][1]) * scale) # x
|
|
)
|
|
return start_point
|