aidlab/AiDA_Python
6
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat 结果图宽度自适应

Browse Source 
fix
This commit is contained in:
zhouchengrong
2024-08-02 11:17:07 +08:00
parent 1bb1e1d649
commit 7254f3ae31
2 changed files with 27 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
app/service/design/utils/synthesis_item.py
View File

@@ -63,14 +63,18 @@ def synthesis(data, size, basic_info):
# 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':
body_mask = np.array(d['image'].split()[3])
left_shoulder = basic_info['body_point_test']['shoulder_left']
right_shoulder = basic_info['body_point_test']['shoulder_right']
# 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['position'][1], d['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['position'][1], d['position'][0]])]
right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['position'][1], d['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)
@@ -118,7 +122,7 @@ def synthesis(data, size, basic_info):
mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
mask_alpha = Image.fromarray(mask_data)
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)
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['image'], (layer['position'][1], layer['position'][0]), layer['image'])