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fix 修复分割后resize时 出现的插值问题，因为时先增加透明通道，然后resize 插值把边缘部分修改为半透明 所以出现缝隙
This commit is contained in:
zhouchengrong
2024-07-18 11:32:58 +08:00
parent ac64cdcc54
commit e0a69b7f63
3 changed files with 26 additions and 41 deletions
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33
app/service/design/items/pipelines/split.py
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@@ -42,39 +42,20 @@ class Split(object):
else: else:
back_mask = result['back_mask'] back_mask = result['back_mask']
rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], result['mask']) # rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], front_mask + back_mask)
rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
new_size = (int(rgba_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(rgba_image.shape[0] * result["scale"] * result["resize_scale"][1]))
rgba_image = cv2.resize(rgba_image, new_size)
result_front_image = np.zeros_like(rgba_image) result_front_image = np.zeros_like(rgba_image)
front_mask = cv2.resize(front_mask, new_size)
result_front_image[front_mask != 0] = rgba_image[front_mask != 0] result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
# TODO PIL resize替换为CV2
# result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
# front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"][1]))
# result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
front_new_size = (int(result_front_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(result_front_image.shape[0] * result["scale"] * result["resize_scale"][1]))
result_front_image = cv2.resize(result_front_image, front_new_size)
result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA)) result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
# result['front_mask_image'] = cv2.resize(front_mask, front_new_size)
# result['front_image'] = result_front_image_pil
front_mask = cv2.resize(front_mask, front_new_size)
result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask) result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask)
if result["name"] in ('blouse', 'dress', 'outwear', 'tops'): if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
result_back_image = np.zeros_like(rgba_image) result_back_image = np.zeros_like(rgba_image)
back_mask = cv2.resize(back_mask, new_size)
result_back_image[back_mask != 0] = rgba_image[back_mask != 0] result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
# TODO PIL resize替换为CV2
# result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
# back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"][1]))
# result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
back_new_size = (int(result_back_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(result_back_image.shape[0] * result["scale"] * result["resize_scale"][1]))
result_back_image = cv2.resize(result_back_image, back_new_size)
result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA)) result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
# result['back_mask_image'] = cv2.resize(back_mask, back_new_size)
# result['back_image'] = result_back_image_pil
back_mask = cv2.resize(back_mask, back_new_size)
result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask) result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask)
else: else:
result['back_image'] = None result['back_image'] = None
@@ -83,7 +64,7 @@ class Split(object):
result['back_mask_image'] = None result['back_mask_image'] = None
# 创建中间图层 # 创建中间图层
result_pattern_image_rgba = rgb_to_rgba((result['pattern_image'].shape[0], result['pattern_image'].shape[1]), result['pattern_image'], result['mask']) result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask'])
result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA)) result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA))
_, result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}') _, result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}')
return result return result

19
app/service/design/utils/conversion_image.py
View File

@@ -10,13 +10,20 @@
import numpy as np import numpy as np
def rgb_to_rgba(rgb_size, rgb_image, mask): # def rgb_to_rgba(rgb_size, rgb_image, mask):
alpha_channel = np.full(rgb_size, 255, dtype=np.uint8) # alpha_channel = np.full(rgb_size, 255, dtype=np.uint8)
# 创建四通道的结果图像 # # 创建四通道的结果图像
# rgba_image = np.dstack((rgb_image, alpha_channel))
# alpha_channel = np.where(mask > 0, 255, 0)
# # 更新RGBA图像的透明度通道
# rgba_image[:, :, 3] = alpha_channel
# return rgba_image
def rgb_to_rgba(rgb_image, mask):
# 创建全透明的alpha通道
alpha_channel = np.where(mask > 0, 255, 0).astype(np.uint8)
# 合并RGB图像和alpha通道
rgba_image = np.dstack((rgb_image, alpha_channel)) rgba_image = np.dstack((rgb_image, alpha_channel))
alpha_channel = np.where(mask > 0, 255, 0)
# 更新RGBA图像的透明度通道
rgba_image[:, :, 3] = alpha_channel
return rgba_image return rgba_image

15
app/service/design/utils/synthesis_item.py
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@@ -101,26 +101,23 @@ def synthesis(data, size):
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'][1], layer['position'][0]), 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(cropped_image, (0, 0), cropped_image) base_image.paste(test_image, (0, 0), test_image)
else: else:
base_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image']) base_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image'])
result_image = base_image result_image = base_image
with io.BytesIO() as output:
result_image.save(output, format='PNG')
data = output.getvalue()
image_data = io.BytesIO() image_data = io.BytesIO()
result_image.save(image_data, format='PNG') result_image.save(image_data, format='PNG')
image_data.seek(0) image_data.seek(0)
# oss upload # oss upload
image_bytes = image_data.read() image_bytes = image_data.read()
bucket_name = 'aida-results' bucket_name = 'test'
# bucket_name= "aida-results"
object_name = f'result_{generate_uuid()}.png' object_name = f'result_{generate_uuid()}.png'
req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
return f"{bucket_name}/{object_name}" return f"{bucket_name}/{object_name}"