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feat 结果图宽度自适应

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fix
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zhouchengrong
2024-08-01 17:46:26 +08:00
parent b75dd7161a
commit 529bb02393
2 changed files with 11 additions and 7 deletions
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2
app/service/design/service.py
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@@ -181,7 +181,7 @@ def upload_images(image_obj):
def update_base_size_priority(layers, size): def update_base_size_priority(layers, size):
# 计算新图片的宽度和高度 # 计算新图片的宽度和高度
max_x = max([layer["position"][1] + layer["image"].size[1] for layer in layers]) max_x = max([layer["position"][1] + layer["image"].size[0] for layer in layers])
min_x = min([layer["position"][1] for layer in layers]) min_x = min([layer["position"][1] for layer in layers])
new_width = max(size[0], max_x - min_x) new_width = max(size[0], max_x - min_x)
new_height = size[1] new_height = size[1]

16
app/service/design/utils/synthesis_item.py
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@@ -63,14 +63,18 @@ def synthesis(data, size, basic_info):
# 创建底图 # 创建底图
base_image = Image.new('RGBA', size, (0, 0, 0, 0)) base_image = Image.new('RGBA', size, (0, 0, 0, 0))
try: try:
all_mask_shape = (size[1], size[0]) all_mask_shape = (size[1], size[0])
body_mask = None body_mask = None
for d in data: for d in data:
if d['name'] == 'body': if d['name'] == 'body':
body_mask = np.array(d['image'].split()[3]) # 创建一个新的宽高透明图像， 把模特贴上去获取mask
left_shoulder = basic_info['body_point_test']['shoulder_left'] transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
right_shoulder = basic_info['body_point_test']['shoulder_right'] transparent_image.paste(d['image'], d['position'], d['image'])
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'])]
right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], d['position'])]
body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255 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) _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
top_outer_mask = np.array(binary_body_mask) top_outer_mask = np.array(binary_body_mask)
@@ -114,13 +118,13 @@ def synthesis(data, size, basic_info):
if layer['image'] is not None: if layer['image'] is not None:
if layer['name'] != "body": if layer['name'] != "body":
test_image = Image.new('RGBA', size, (0, 0, 0, 0)) 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['position'], layer['image'])
mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8) mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
mask_alpha = Image.fromarray(mask_data) mask_alpha = Image.fromarray(mask_data)
cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha) 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)
else: else:
base_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image']) base_image.paste(layer['image'], layer['position'], layer['image'])
result_image = base_image result_image = base_image