561 lines
29 KiB
Python
561 lines
29 KiB
Python
import random
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import cv2
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import numpy as np
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from PIL import Image
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from app.service.utils.new_oss_client import oss_get_image
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class PrintPainting:
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def __init__(self, minio_client):
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self.minio_client = minio_client
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def __call__(self, result):
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single_print = result['print']['single']
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overall_print = result['print']['overall']
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element_print = result['print']['element']
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partial_path = result['print']['partial'] if 'partial' in result['print'] else None
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result['single_image'] = None
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result['print_image'] = None
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# TODO 给result['pattern_image'] resize 到resize_scale的大小
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# TODO 给result['mask'] resize 到resize_scale的大小
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if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
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pass
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else:
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# 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
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height, width = result['pattern_image'].shape[:2]
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new_width = int(width * result['resize_scale'][0])
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new_height = int(height * result['resize_scale'][1])
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result['pattern_image'] = cv2.resize(result['pattern_image'], (new_width, new_height))
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result['final_image'] = cv2.resize(result['final_image'], (new_width, new_height))
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result['mask'] = cv2.resize(result['mask'], (new_width, new_height))
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result['gray'] = cv2.resize(result['gray'], (new_width, new_height))
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if overall_print['print_path_list']:
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overall_print['location'][0] = [x * y for x, y in zip(overall_print['location'][0], result['resize_scale'])]
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painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]}
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result['print_image'] = result['pattern_image']
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# 获取平铺 + 旋转 的overall print
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painting_dict = self.painting_collection(painting_dict, overall_print)
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result['print_image'] = self.printpaint(result, painting_dict, print_=True)
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result['single_image'] = result['final_image'] = result['pattern_image'] = result['print_image']
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if single_print['print_path_list']:
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# 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
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sketch_resize_scale = result['resize_scale']
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print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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for i in range(len(single_print['print_path_list'])):
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image, image_mode = self.read_image(single_print['print_path_list'][i])
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if image_mode == "RGB":
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image_rgba = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA)
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image = Image.fromarray(image_rgba)
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new_size = (int(result['pattern_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['pattern_image'].shape[0] * single_print['print_scale_list'][i][1]))
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mask = image.split()[3]
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resized_source = image.resize(new_size)
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resized_source_mask = mask.resize(new_size)
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rotated_resized_source = resized_source.rotate(-single_print['print_angle_list'][i])
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rotated_resized_source_mask = resized_source_mask.rotate(-single_print['print_angle_list'][i])
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source_image_pil = Image.fromarray(cv2.cvtColor(print_background, cv2.COLOR_BGR2RGB))
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source_image_pil_mask = Image.fromarray(cv2.cvtColor(mask_background, cv2.COLOR_BGR2RGB))
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source_image_pil.paste(rotated_resized_source, (int(single_print['location'][i][0] * sketch_resize_scale[0]), int(single_print['location'][i][1] * sketch_resize_scale[1])), rotated_resized_source)
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source_image_pil_mask.paste(rotated_resized_source_mask, (int(single_print['location'][i][0] * sketch_resize_scale[0]), int(single_print['location'][i][1] * sketch_resize_scale[1])), rotated_resized_source_mask)
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print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR)
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mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR)
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ret, mask_background = cv2.threshold(mask_background, 124, 255, cv2.THRESH_BINARY)
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print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY))
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img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask)
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img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=cv2.bitwise_not(print_mask))
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mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2)
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gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
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img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8) # 当sketch 图像为灰色时(非纯白) ， 印花*灰度图像会导致印花在sketch上颜色变暗
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# img_fg = (img_fg * (mask_mo / 255) ).astype(np.uint8) # 不过灰度图像
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result['final_image'] = cv2.add(img_bg, img_fg)
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canvas = np.full_like(result['final_image'], 255)
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temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
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tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
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temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
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tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
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result['single_image'] = cv2.add(tmp1, tmp2)
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if element_print['element_path_list']:
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# 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
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sketch_resize_scale = result['resize_scale']
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print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
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mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
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for i in range(len(element_print['element_path_list'])):
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image, image_mode = self.read_image(element_print['element_path_list'][i])
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if image_mode == "RGBA":
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new_size = (int(result['final_image'].shape[1] * element_print['element_scale_list'][i][0]), int(result['final_image'].shape[0] * element_print['element_scale_list'][i][1]))
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mask = image.split()[3]
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resized_source = image.resize(new_size)
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resized_source_mask = mask.resize(new_size)
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rotated_resized_source = resized_source.rotate(-element_print['element_angle_list'][i])
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rotated_resized_source_mask = resized_source_mask.rotate(-element_print['element_angle_list'][i])
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source_image_pil = Image.fromarray(cv2.cvtColor(print_background, cv2.COLOR_BGR2RGB))
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source_image_pil_mask = Image.fromarray(cv2.cvtColor(mask_background, cv2.COLOR_BGR2RGB))
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source_image_pil.paste(rotated_resized_source, (int(element_print['location'][i][0] * sketch_resize_scale[0]), int(element_print['location'][i][1] * sketch_resize_scale[1])), rotated_resized_source)
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source_image_pil_mask.paste(rotated_resized_source_mask, (int(element_print['location'][i][0] * sketch_resize_scale[0]), int(element_print['location'][i][1] * sketch_resize_scale[1])), rotated_resized_source_mask)
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print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR)
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mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR)
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else:
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mask = self.get_mask_inv(image)
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mask = np.expand_dims(mask, axis=2)
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mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
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mask = cv2.bitwise_not(mask)
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mask = cv2.resize(mask, (int(result['final_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[0] * single_print['print_scale_list'][i][1])))
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image = cv2.resize(image, (int(result['final_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[0] * single_print['print_scale_list'][i][1])))
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# 旋转后的坐标需要重新算
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rotate_mask, _ = self.img_rotate(mask, element_print['element_angle_list'][i])
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rotate_image, rotated_new_size = self.img_rotate(image, element_print['element_angle_list'][i])
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# x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2)
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x, y = int(element_print['location'][i][0] - rotated_new_size[0]), int(element_print['location'][i][1] - rotated_new_size[1])
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image_x = print_background.shape[1]
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image_y = print_background.shape[0]
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print_x = rotate_image.shape[1]
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print_y = rotate_image.shape[0]
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if x <= 0:
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rotate_image = rotate_image[:, -x:]
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rotate_mask = rotate_mask[:, -x:]
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start_x = x = 0
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else:
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start_x = x
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if y <= 0:
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rotate_image = rotate_image[-y:, :]
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rotate_mask = rotate_mask[-y:, :]
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start_y = y = 0
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else:
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start_y = y
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if x + print_x > image_x:
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rotate_image = rotate_image[:, :image_x - x]
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rotate_mask = rotate_mask[:, :image_x - x]
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if y + print_y > image_y:
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rotate_image = rotate_image[:image_y - y, :]
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rotate_mask = rotate_mask[:image_y - y, :]
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mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x)
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print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x)
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# gray_image = cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)
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# print_background = cv2.bitwise_and(print_background, print_background, mask=gray_image)
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print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY))
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img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask)
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# TODO element 丢失信息
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three_channel_image = cv2.merge([cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask)])
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img_bg = cv2.bitwise_and(result['final_image'], three_channel_image)
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# mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2)
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# gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
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# img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8)
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result['final_image'] = cv2.add(img_bg, img_fg)
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canvas = np.full_like(result['final_image'], 255)
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temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
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tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
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temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
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tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
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result['single_image'] = cv2.add(tmp1, tmp2)
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if partial_path:
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print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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image, image_mode = self.read_image(partial_path)
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if image_mode == "RGBA":
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new_size = (result['pattern_image'].shape[1], result['pattern_image'].shape[0])
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mask = image.split()[3]
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resized_source = image.resize(new_size)
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resized_source_mask = mask.resize(new_size)
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# rotated_resized_source = resized_source.rotate(-partial_print['print_angle_list'][i])
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# rotated_resized_source_mask = resized_source_mask.rotate(-partial_print['print_angle_list'][i])
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source_image_pil = Image.fromarray(cv2.cvtColor(print_background, cv2.COLOR_BGR2RGB))
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source_image_pil_mask = Image.fromarray(cv2.cvtColor(mask_background, cv2.COLOR_BGR2RGB))
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source_image_pil.paste(resized_source, (0, 0), resized_source)
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source_image_pil_mask.paste(resized_source_mask, (0, 0), resized_source_mask)
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print_background = cv2.cvtColor(np.array(source_image_pil), cv2.COLOR_RGBA2BGR)
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mask_background = cv2.cvtColor(np.array(source_image_pil_mask), cv2.COLOR_RGBA2BGR)
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ret, mask_background = cv2.threshold(mask_background, 124, 255, cv2.THRESH_BINARY)
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print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY))
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img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask)
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three_channel_image = cv2.merge([cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask), cv2.bitwise_not(print_mask)])
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img_bg = cv2.bitwise_and(result['final_image'], three_channel_image)
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result['final_image'] = cv2.add(img_bg, img_fg)
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canvas = np.full_like(result['final_image'], 255)
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temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
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tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
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temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
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tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
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result['single_image'] = cv2.add(tmp1, tmp2)
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return result
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@staticmethod
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def stack_prin(print_background, pattern_image, rotate_image, start_y, y, start_x, x):
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temp_print = np.zeros((pattern_image.shape[0], pattern_image.shape[1], 3), dtype=np.uint8)
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temp_print[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image
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img2gray = cv2.cvtColor(temp_print, cv2.COLOR_BGR2GRAY)
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ret, mask_ = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)
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mask_inv = cv2.bitwise_not(mask_)
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img1_bg = cv2.bitwise_and(print_background, print_background, mask=mask_inv)
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img2_fg = cv2.bitwise_and(temp_print, temp_print, mask=mask_)
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print_background = img1_bg + img2_fg
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return print_background
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def painting_collection(self, painting_dict, print_dict):
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print_ = self.get_print(print_dict)
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painting_dict['location'] = print_['location']
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dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w'])
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dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5))
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gap = print_dict.get('gap', [[0, 0]])[0]
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painting_dict['tile_print'] = tile_image(pattern=print_['image'],
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dim=dim_pattern,
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gap_x=gap[0],
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gap_y=gap[1],
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canvas_h=painting_dict['dim_image_h'],
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canvas_w=painting_dict['dim_image_w'],
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location=painting_dict['location'],
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angle=int(print_.get('print_angle_list', [0])[0]))
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painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8)
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return painting_dict
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def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False):
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tile = None
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if not trigger:
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tile = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA)
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else:
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resize_pattern = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA)
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if len(pattern.shape) == 2:
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tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4))
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if len(pattern.shape) == 3:
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tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4, 1))
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tile = self.crop_image(tile, dim_image_h, dim_image_w, location, resize_pattern.shape)
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return tile
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def get_mask_inv(self, print_):
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if print_[0][0][0] == 255 and print_[0][0][1] == 255 and print_[0][0][2] == 255:
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bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0]
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print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)
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bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2]
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bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True)
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bg_a_high, bg_a_low = self.get_low_high_lab(bg_a)
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bg_b_high, bg_b_low = self.get_low_high_lab(bg_b)
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lower = np.array([bg_L_low, bg_a_low, bg_b_low])
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upper = np.array([bg_L_high, bg_a_high, bg_b_high])
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mask_inv = cv2.inRange(print_tile, lower, upper)
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return mask_inv
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else:
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mask_inv = np.zeros(print_.shape[:2], dtype=np.uint8)
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return mask_inv
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@staticmethod
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def printpaint(result, painting_dict, print_=False):
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if print_:
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print_mask = cv2.bitwise_and(result['mask'], cv2.bitwise_not(painting_dict['mask_inv_print']))
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img_fg = cv2.bitwise_and(painting_dict['tile_print'], painting_dict['tile_print'], mask=print_mask)
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else:
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print_mask = result['mask']
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img_fg = result['final_image']
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# if print_ and not painting_dict['Trigger']:
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# index_ = None
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# try:
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# index_ = len(painting_dict['location'])
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# except:
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# assert f'there must be parameter of location if choose IfSingle'
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#
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# for i in range(index_):
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# start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0])
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#
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# length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0])
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# length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1])
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#
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# change_region = img_fg[start_h: length_h, start_w: length_w, :]
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# # problem in change_mask
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# change_mask = print_mask[start_h: length_h, start_w: length_w]
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# # get real part into change mask
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# _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY)
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# cv2.bitwise_not(painting_dict['mask_inv_print'])
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# img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region
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clothes_mask_print = cv2.bitwise_not(print_mask)
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img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=clothes_mask_print)
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mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2)
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gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
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img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8)
|
||
print_image = cv2.add(img_bg, img_fg)
|
||
return print_image
|
||
|
||
def get_print(self, print_dict):
|
||
if 'print_scale_list' not in print_dict.keys() or print_dict['print_scale_list'][0][0] < 0.3:
|
||
print_dict['scale'] = 0.3
|
||
else:
|
||
print_dict['scale'] = print_dict['print_scale_list'][0][0]
|
||
|
||
bucket_name = print_dict['print_path_list'][0].split("/", 1)[0]
|
||
object_name = print_dict['print_path_list'][0].split("/", 1)[1]
|
||
image = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="PIL")
|
||
# 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
|
||
if image.mode == "RGBA":
|
||
new_background = Image.new('RGB', image.size, (255, 255, 255))
|
||
new_background.paste(image, mask=image.split()[3])
|
||
image = new_background
|
||
print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
|
||
return print_dict
|
||
|
||
def crop_image(self, image, image_size_h, image_size_w, location, print_shape):
|
||
print_w = print_shape[1]
|
||
print_h = print_shape[0]
|
||
|
||
# 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量
|
||
# 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可
|
||
x_offset = print_w - int(location[0][1] % print_w) + print_w // 2
|
||
y_offset = print_h - int(location[0][0] % print_h) + print_h // 2
|
||
|
||
# y_offset = int(location[0][0])
|
||
# x_offset = int(location[0][1])
|
||
|
||
if len(image.shape) == 2:
|
||
image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w]
|
||
elif len(image.shape) == 3:
|
||
image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :]
|
||
return image
|
||
|
||
@staticmethod
|
||
def get_low_high_lab(Lab_value, L=False):
|
||
if L:
|
||
high = Lab_value + 30 if Lab_value + 30 < 255 else 255
|
||
low = Lab_value - 30 if Lab_value - 30 > 0 else 0
|
||
else:
|
||
high = Lab_value + 30 if Lab_value + 30 < 255 else 255
|
||
low = Lab_value - 30 if Lab_value - 30 > 0 else 0
|
||
return high, low
|
||
|
||
@staticmethod
|
||
def img_rotate(image, angel):
|
||
"""顺时针旋转图像任意角度
|
||
|
||
Args:
|
||
image (np.array): [原始图像]
|
||
angel (float): [逆时针旋转的角度]
|
||
|
||
Returns:
|
||
[array]: [旋转后的图像]
|
||
"""
|
||
|
||
h, w = image.shape[:2]
|
||
center = (w // 2, h // 2)
|
||
# if type(angel) is not int:
|
||
# angel = 0
|
||
M = cv2.getRotationMatrix2D(center, -angel, 1)
|
||
# 调整旋转后的图像长宽
|
||
rotated_h = int((w * np.abs(M[0, 1]) + (h * np.abs(M[0, 0]))))
|
||
rotated_w = int((h * np.abs(M[0, 1]) + (w * np.abs(M[0, 0]))))
|
||
M[0, 2] += (rotated_w - w) // 2
|
||
M[1, 2] += (rotated_h - h) // 2
|
||
# 旋转图像
|
||
rotated_img = cv2.warpAffine(image, M, (rotated_w, rotated_h))
|
||
|
||
return rotated_img, ((rotated_img.shape[1] - image.shape[1]) // 2, (rotated_img.shape[0] - image.shape[0]) // 2)
|
||
# return rotated_img, (0, 0)
|
||
|
||
@staticmethod
|
||
def rotate_crop_image(img, angle, crop):
|
||
"""
|
||
angle: 旋转的角度
|
||
crop: 是否需要进行裁剪，布尔向量
|
||
"""
|
||
if not isinstance(crop, bool):
|
||
raise ValueError("The 'crop' parameter must be a boolean.")
|
||
|
||
crop_image = lambda img, x0, y0, w, h: img[y0:y0 + h, x0:x0 + w]
|
||
h, w = img.shape[:2]
|
||
# 旋转角度的周期是360°
|
||
angle %= 360
|
||
# 计算仿射变换矩阵
|
||
M_rotation = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1)
|
||
# 得到旋转后的图像
|
||
img_rotated = cv2.warpAffine(img, M_rotation, (w, h))
|
||
|
||
# 如果需要去除黑边
|
||
if crop:
|
||
# 裁剪角度的等效周期是180°
|
||
angle_crop = angle % 180
|
||
if angle_crop > 90:
|
||
angle_crop = 180 - angle_crop
|
||
# 转化角度为弧度
|
||
theta = angle_crop * np.pi / 180
|
||
# 计算高宽比
|
||
hw_ratio = float(h) / float(w)
|
||
# 计算裁剪边长系数的分子项
|
||
tan_theta = np.tan(theta)
|
||
numerator = np.cos(theta) + np.sin(theta) * np.tan(theta)
|
||
|
||
# 计算分母中和高宽比相关的项
|
||
r = hw_ratio if h > w else 1 / hw_ratio
|
||
# 计算分母项
|
||
denominator = r * tan_theta + 1
|
||
# 最终的边长系数
|
||
crop_mult = numerator / denominator
|
||
|
||
# 得到裁剪区域
|
||
w_crop = int(crop_mult * w)
|
||
h_crop = int(crop_mult * h)
|
||
x0 = int((w - w_crop) / 2)
|
||
y0 = int((h - h_crop) / 2)
|
||
|
||
img_rotated = crop_image(img_rotated, x0, y0, w_crop, h_crop)
|
||
|
||
return img_rotated
|
||
|
||
def read_image(self, image_url):
|
||
image = oss_get_image(oss_client=self.minio_client, bucket=image_url.split("/", 1)[0], object_name=image_url.split("/", 1)[1], data_type="cv2")
|
||
if image.shape[2] == 4:
|
||
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
|
||
image = Image.fromarray(image_rgb)
|
||
image_mode = "RGBA"
|
||
else:
|
||
image_mode = "RGB"
|
||
return image, image_mode
|
||
|
||
@staticmethod
|
||
def resize_and_crop(img, target_width, target_height):
|
||
# 获取原始图像的尺寸
|
||
original_height, original_width = img.shape[:2]
|
||
|
||
# 计算目标尺寸的宽高比
|
||
target_ratio = target_width / target_height
|
||
|
||
# 计算原始图像的宽高比
|
||
original_ratio = original_width / original_height
|
||
|
||
# 调整尺寸
|
||
if original_ratio > target_ratio:
|
||
# 原始图像更宽，按高度resize，然后裁剪宽度
|
||
new_height = target_height
|
||
new_width = int(original_width * (target_height / original_height))
|
||
resized_img = cv2.resize(img, (new_width, new_height))
|
||
# 裁剪宽度
|
||
start_x = (new_width - target_width) // 2
|
||
cropped_img = resized_img[:, start_x:start_x + target_width]
|
||
else:
|
||
# 原始图像更高，按宽度resize，然后裁剪高度
|
||
new_width = target_width
|
||
new_height = int(original_height * (target_width / original_width))
|
||
resized_img = cv2.resize(img, (new_width, new_height))
|
||
# 裁剪高度
|
||
start_y = (new_height - target_height) // 2
|
||
cropped_img = resized_img[start_y:start_y + target_height, :]
|
||
|
||
return cropped_img
|
||
|
||
|
||
def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0):
|
||
"""
|
||
按照指定的 X/Y 间距平铺印花，并支持旋转
|
||
:param angle: 旋转角度 (度数, 逆时针)
|
||
"""
|
||
# 1. 确保输入是 RGBA
|
||
if pattern.shape[2] == 3:
|
||
pattern = cv2.cvtColor(pattern, cv2.COLOR_BGR2BGRA)
|
||
|
||
# 2. 缩放与旋转印花
|
||
resized_p = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA)
|
||
rotated_p = rotate_image(resized_p, angle)
|
||
p_h, p_w = rotated_p.shape[:2]
|
||
|
||
# 3. 创建透明单元格
|
||
cell_h, cell_w = p_h + gap_y, p_w + gap_x
|
||
unit_cell = np.zeros((cell_h, cell_w, 4), dtype=np.uint8)
|
||
unit_cell[:p_h, :p_w, :] = rotated_p
|
||
|
||
# 4. 执行平铺
|
||
tiles_y = (canvas_h // cell_h) + 2
|
||
tiles_x = (canvas_w // cell_w) + 2
|
||
full_tiled = np.tile(unit_cell, (tiles_y, tiles_x, 1))
|
||
|
||
# 5. 裁剪平铺层
|
||
offset_x = int(location[0][1] % cell_w)
|
||
offset_y = int(location[0][0] % cell_h)
|
||
tiled_layer = full_tiled[offset_y: offset_y + canvas_h,
|
||
offset_x: offset_x + canvas_w]
|
||
|
||
# 6. 创建纯白色背景并合成
|
||
# 创建一个纯白色的 BGR 画布
|
||
white_background = np.full((canvas_h, canvas_w, 3), 255, dtype=np.uint8)
|
||
|
||
# 分离平铺层的颜色通道和 Alpha 通道
|
||
tiled_bgr = tiled_layer[:, :, :3]
|
||
alpha_mask = tiled_layer[:, :, 3] / 255.0 # 归一化到 0-1
|
||
alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask]) # 扩展到 3 通道
|
||
|
||
# 执行 Alpha 混合：结果 = 平铺层 * alpha + 背景 * (1 - alpha)
|
||
result = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8)
|
||
|
||
return result
|
||
|
||
|
||
def rotate_image(image, angle):
|
||
"""
|
||
旋转图片并保持完整内容（自动扩大画布）
|
||
"""
|
||
if angle == 0:
|
||
return image
|
||
|
||
(h, w) = image.shape[:2]
|
||
(cX, cY) = (w // 2, h // 2)
|
||
|
||
# 获取旋转矩阵
|
||
M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0)
|
||
|
||
# 计算旋转后新边界的 sine 和 cosine
|
||
cos = np.abs(M[0, 0])
|
||
sin = np.abs(M[0, 1])
|
||
|
||
# 计算新的画布尺寸
|
||
nW = int((h * sin) + (w * cos))
|
||
nH = int((h * cos) + (w * sin))
|
||
|
||
# 调整旋转矩阵以考虑平移
|
||
M[0, 2] += (nW / 2) - cX
|
||
M[1, 2] += (nH / 2) - cY
|
||
|
||
# 执行旋转
|
||
return cv2.warpAffine(image, M, (nW, nH))
|
||
|
||
|
||
def crop_image(image, image_size_h, image_size_w, location, print_shape):
|
||
print_w = print_shape[1]
|
||
print_h = print_shape[0]
|
||
|
||
# 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量
|
||
# 偏移量增加2分之print.w 使坐标位于图中间 如果要位于左上角删除+ print_w // 2 即可
|
||
x_offset = print_w - int(location[0][1] % print_w) + print_w // 2
|
||
y_offset = print_h - int(location[0][0] % print_h) + print_h // 2
|
||
|
||
# y_offset = int(location[0][0])
|
||
# x_offset = int(location[0][1])
|
||
|
||
if len(image.shape) == 2:
|
||
image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w]
|
||
elif len(image.shape) == 3:
|
||
image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :]
|
||
return image
|