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):

This commit is contained in:
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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@@ -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")