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feat: 新增design模式 merge,前端CV python 合成

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zcr
2026-01-12 16:18:04 +08:00
parent fe12b5697d
commit 40b57b749c
9 changed files with 355 additions and 173 deletions
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105
app/service/design_fast/utils/synthesis_item.py
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@@ -187,6 +187,111 @@ def synthesis(data, size, basic_info):
logging.warning(f"synthesis runtime exception : {e}")
def merge(data, size, basic_info):
# out_of_bounds_control: 是否允许服装越界 True 允许 False 不允许 默认情况允许
out_of_bounds_control = basic_info.get('out_of_bounds_control', True)
# 创建底图
base_image = Image.new('RGBA', size, (0, 0, 0, 0))
try:
all_mask_shape = (size[1], size[0])
body_mask = None
for d in data:
if d['name'] == 'body' or d['name'] == 'mannequin':
# 创建一个新的宽高透明图像， 把模特贴上去获取mask
transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image']) # 此处可变数组会被paste篡改值，所以使用下标获取position
body_mask = np.array(transparent_image.split()[3])
# 根据新的坐标获取新的肩点
left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255
_, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
top_outer_mask = np.array(binary_body_mask)
bottom_outer_mask = np.array(binary_body_mask)
others_outer_mask = np.array(binary_body_mask)
top = True
bottom = True
others = True
i = len(data)
while i:
i -= 1
if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
if out_of_bounds_control:
top = True
else:
top = False
mask_shape = data[i]['mask'].shape
y_offset, x_offset = data[i]['adaptive_position']
# 初始化叠加区域的起始和结束位置
all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
# 将叠加区域赋值为相应的像素值
_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
background = np.zeros_like(top_outer_mask)
background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
top_outer_mask = background + top_outer_mask
elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
# bottom = False
mask_shape = data[i]['mask'].shape
y_offset, x_offset = data[i]['adaptive_position']
# 初始化叠加区域的起始和结束位置
all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
# 将叠加区域赋值为相应的像素值
_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
background = np.zeros_like(top_outer_mask)
background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
bottom_outer_mask = background + bottom_outer_mask
elif others and data[i]['name'] in ['others_front']:
mask_shape = data[i]['mask'].shape
y_offset, x_offset = data[i]['adaptive_position']
# 初始化叠加区域的起始和结束位置
all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
# 将叠加区域赋值为相应的像素值
_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
background = np.zeros_like(top_outer_mask)
background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
others_outer_mask = background + others_outer_mask
pass
elif bottom is False and top is False:
break
all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask)
all_mask = cv2.bitwise_or(all_mask, others_outer_mask)
for layer in data:
if layer['image'] is not None:
if layer['name'] != "body":
test_image = Image.new('RGBA', size, (0, 0, 0, 0))
paste_img, position = transpose_rotate(layer, layer['image'])
test_image.paste(paste_img, position, paste_img)
mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
mask_alpha = Image.fromarray(mask_data)
mask_alpha.paste(paste_img.getchannel('A'), position, paste_img.getchannel('A'))
cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha)
base_image.paste(test_image, (0, 0), cropped_image) # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00
else:
base_image.paste(layer['merge_image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['merge_image'])
result_image = base_image
image_data = io.BytesIO()
result_image.save(image_data, format='PNG')
image_data.seek(0)
# oss upload
image_bytes = image_data.read()
bucket_name = "aida-results"
object_name = f'result_{generate_uuid()}.png'
oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
return f"{bucket_name}/{object_name}"
except Exception as e:
logging.warning(f"synthesis runtime exception : {e}")
def synthesis_single(front_image, back_image):
result_image = None
if front_image: