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feat 结果图宽度自适应

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fix
This commit is contained in:
zhouchengrong
2024-08-02 17:12:34 +08:00
parent 3a0e175e61
commit 281f812636
2 changed files with 8 additions and 8 deletions
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14
app/service/design/utils/synthesis_item.py
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@@ -69,12 +69,12 @@ def synthesis(data, size, basic_info):
if d['name'] == 'body':
# 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
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['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]])]
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)
@@ -88,7 +88,7 @@ def synthesis(data, size, basic_info):
if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
top = False
mask_shape = data[i]['mask'].shape
y_offset, x_offset = data[i]['position']
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)
@@ -100,7 +100,7 @@ def synthesis(data, size, basic_info):
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]['position']
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)
@@ -118,13 +118,13 @@ def synthesis(data, size, basic_info):
if layer['image'] is not None:
if layer['name'] != "body":
test_image = Image.new('RGBA', size, (0, 0, 0, 0))
test_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image'])
test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
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) # 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'])
base_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
result_image = base_image