aidlab/AiDA_Python
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feat（新功能）: 1、design-print为解决sketch原图太灰导致印花颜色便暗 2、cv2.resize 插值算法更换，提升resize后图片质量 fix（修复bug）: refactor（重构）: test(增加测试):

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zhh
2025-09-26 10:29:39 +08:00
parent 4bc79e62ca
commit 0d4d464e3f
2 changed files with 39 additions and 8 deletions
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33
app/service/design_fast/pipeline/loading.py
View File

@@ -1,5 +1,6 @@
import io
import logging
import os
import cv2
import numpy as np
@@ -38,12 +39,42 @@ class LoadImage:
def __call__(self, result):
result['image'], result['pre_mask'] = self.read_image(result['path'])
result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
# if 'extract_lines' in result.keys():
# if result['extract_lines']:
# result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY), result['path'])
# else:
# result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
# else:
# result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY), result['path'])
result['keypoint'] = self.get_keypoint(result['name'])
result['img_shape'] = result['image'].shape
result['ori_shape'] = result['image'].shape
return result
def get_lines(self, img, path):
binary = cv2.adaptiveThreshold(img, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV,
25, 10)
# 步骤2：细化边缘（可选，让线条更干净）
# kernel = np.ones((1, 1), np.uint8)
# clean = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
thinned = cv2.ximgproc.thinning(binary, thinningType=cv2.ximgproc.THINNING_ZHANGSUEN) # thinning算法细化线条
mask = thinned > 0
result = np.ones_like(img) * 255
result[mask] = img[mask]
# 步骤3：反转回 白底黑线
# lines = cv2.bitwise_not(thinned)
# cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Original_{path.replace('/', '-')}.png"), img)
# cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Line_{path.replace('/', '-')}.png"), result)
return result
def read_image(self, image_path):
image_mask = None
image = oss_get_image(oss_client=self.minio_client, bucket=image_path.split("/", 1)[0], object_name=image_path.split("/", 1)[1], data_type="cv2")

14
app/service/design_fast/pipeline/split.py
View File

@@ -32,14 +32,14 @@ class Split(object):
new_width = int(width * result['resize_scale'][0])
new_height = int(height * result['resize_scale'][1])
front_mask = cv2.resize(result['front_mask'], (new_width, new_height))
back_mask = cv2.resize(result['back_mask'], (new_width, new_height))
front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
rgba_image = cv2.resize(rgba_image, new_size)
rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA)
result_front_image = np.zeros_like(rgba_image)
front_mask = cv2.resize(front_mask, new_size)
front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA)
result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
if 'transparent' in result.keys():
@@ -48,7 +48,7 @@ class Split(object):
if transparent['mask_url'] is not None and transparent['mask_url'] != "":
# 预处理用户自选区mask
seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2")
seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_NEAREST)
seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_AREA)
# 转换颜色空间为 RGB（OpenCV 默认是 BGR）
image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB)
@@ -75,7 +75,7 @@ class Split(object):
# if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
# result_back_image = np.zeros_like(rgba_image)
# back_mask = cv2.resize(back_mask, new_size)
# back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
# result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
# result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
# result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
@@ -104,7 +104,7 @@ class Split(object):
# # result['back_mask_image'] = None
result_back_image = np.zeros_like(rgba_image)
back_mask = cv2.resize(back_mask, new_size)
back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)