贵州省网站备案,企业网站做静态网站还是,网站部分链接做301跳转,网页制作成app上一篇博客【「图像 merge」无中生有制造数据 】写的是图片直接融合#xff0c;此方法生成的图片相对而言比较生硬#xff0c;虽然目标图片已经透明化处理过了#xff0c;但是生成的图片依旧很假 除了上述上述的图片叠加融合之外#xff0c;还有一种更加自然的融合方法此方法生成的图片相对而言比较生硬虽然目标图片已经透明化处理过了但是生成的图片依旧很假 除了上述上述的图片叠加融合之外还有一种更加自然的融合方法就是 cv2.seamlessClone 生成的效果图如下图所示 但是 cv2.seamlessClone 并不是万能的需要根据实际情况测试页根据目标模版的制作效果有很大关系
注意! 此方法融合图片时目标区域不能按照目标的边缘进行透明化抠图需要包含一部分的边缘信息不然融合效果会很差 此算法的 目标图透明化处理/抠图处理与 【「图像 merge」无中生有制造数据 】一致相关代码已附在博客中自行移步查看
# !/usr/bin/env python
# -*- coding:utf-8 -*-
# Time : 2023.10
# Author : 绿色羽毛
# Email : lvseyumaofoxmail.com
# Blog : https://blog.csdn.net/ViatorSun
# Note :import os
import cv2
import random
from random import sample
import numpy as np
import argparsedef read_label_txt(label_dir):labels []with open(label_dir) as fp:for f in fp.readlines():labels.append(f.strip().split( ))return labelsdef rescale_yolo_labels(labels, img_shape):height, width, nchannel img_shaperescale_boxes []for box in list(labels):x_c float(box[1]) * widthy_c float(box[2]) * heightw float(box[3]) * widthh float(box[4]) * heightx_left x_c - w * .5y_left y_c - h * .5x_right x_c w * .5y_right y_c h * .5rescale_boxes.append([box[0], int(x_left), int(y_left), int(x_right), int(y_right)])return rescale_boxesdef xyxy2xywh(image, bboxes):height, width, _ image.shapeboxes []for box in bboxes:if len(box) 4:continuecls int(box[0])x_min box[1]y_min box[2]x_max box[3]y_max box[4]w x_max - x_minh y_max - y_minx_c (x_min x_max) / 2.0y_c (y_min y_max) / 2.0x_c x_c / widthy_c y_c / heightw float(w) / widthh float(h) / heightboxes.append([cls, x_c, y_c, w, h])return boxesdef cast_color(img, value):img_t cv2.cvtColor(img,cv2.COLOR_BGR2HSV)h,s,v cv2.split(img_t)# 增加图像对比度v2 np.clip(cv2.add(2*v,value),0,255)img2 np.uint8(cv2.merge((h,s,v2)))img_cast cv2.cvtColor(img2,cv2.COLOR_HSV2BGR) # 改变图像对比度return img_castdef brightness(img, value):img_t cv2.cvtColor(img,cv2.COLOR_BGR2HSV)h,s,v cv2.split(img_t)# 增加图像亮度v1 np.clip(cv2.add(1*v,value),0,255)img1 np.uint8(cv2.merge((h,s,v1)))img_brightness cv2.cvtColor(img1,cv2.COLOR_HSV2BGR) # 改变图像亮度亮度return img_brightnessdef random_add_patches_on_objects(image, template_lst, rescale_boxes, mask_lst, paste_number):img image.copy()new_bboxes []cl 0random.shuffle(rescale_boxes)for rescale_bbox in rescale_boxes[:int(len(rescale_boxes) * 0.2)]: # 待ps图像 目标框中num_p random.randint(0, 50) % len(template_lst) # 随机挑选 原图和maskp_img template_lst[num_p]mask mask_lst[num_p]bbox_h, bbox_w, bbox_c p_img.shapeobj_xmin rescale_bbox[1]obj_ymin rescale_bbox[2]obj_xmax rescale_bbox[3]obj_ymax rescale_bbox[4]obj_w obj_xmax - obj_xmin 1 # 目标框尺寸obj_h obj_ymax - obj_ymin 1new_bbox_w bbox_wnew_bbox_h bbox_hwhile not (bbox_w obj_w and bbox_h obj_h): # 如果目标框小于 mask尺寸对mask进行缩放以确保可以放进 bbox中new_bbox_w int(bbox_w * random.uniform(0.5, 0.8))new_bbox_h int(bbox_h * random.uniform(0.5, 0.8))bbox_w, bbox_h new_bbox_w, new_bbox_hsuccess_num 0while success_num paste_number:center_search_space [obj_xmin, obj_ymin, obj_xmax - new_bbox_w - 1, obj_ymax - new_bbox_h - 1] # 选取生成随机点区域if center_search_space[0] center_search_space[2] or center_search_space[1] center_search_space[3]:print( center_search_space error!!!! )success_num 1continuenew_bbox_x_min random.randint(center_search_space[0], center_search_space[2]) # 随机生成点坐标new_bbox_y_min random.randint(center_search_space[1], center_search_space[3])new_bbox_x_left, new_bbox_y_top, new_bbox_x_right, new_bbox_y_bottom new_bbox_x_min, new_bbox_y_min, new_bbox_x_min new_bbox_w - 1, new_bbox_y_min new_bbox_h - 1new_bbox [cl, int(new_bbox_x_left), int(new_bbox_y_top), int(new_bbox_x_right), int(new_bbox_y_bottom)]success_num 1new_bboxes.append(new_bbox)mask cv2.resize(mask, (new_bbox_w, new_bbox_h)) p_img cv2.resize(p_img, (new_bbox_w, new_bbox_h))center (int(new_bbox_w / 2), int(new_bbox_h / 2))img[new_bbox_y_top:new_bbox_y_bottom, new_bbox_x_left:new_bbox_x_right] cv2.seamlessClone(p_img,image[new_bbox_y_top:new_bbox_y_bottom, new_bbox_x_left:new_bbox_x_right],mask, center, cv2.MONOCHROME_TRANSFER) # NORMAL_CLONE 、MIXED_CLONE 和 MONOCHROME_TRANSFERreturn img, new_bboxesif __name__ __main__:# 用来装载参数的容器parser argparse.ArgumentParser(descriptionPS)# 给这个解析对象添加命令行参数parser.add_argument(-i, --images, default /media/yinzhe/DataYZ/DataSet/DataSet/bag_model,typestr, helppath of images)parser.add_argument(-t, --templates, default /media/yinzhe/DataYZ/DataSet/DataSet/bag_mask,typestr, helppath of templates)parser.add_argument(-s, --saveImage,default /media/yinzhe/DataYZ/DataSet/DataSet/bag_save3, typestr, helppath of )parser.add_argument(-n, --num, default5, typestr, helpnumber of img)args parser.parse_args() # 获取所有参数templates_path args.templatesimages_path args.imagessave_path args.saveImagenum int(args.num)template_paths []if not os.path.exists(save_path):os.makedirs(save_path)for t_path in os.listdir(templates_path):template_paths.append(t_path)# template_paths random.shuffle(template_paths) #打乱顺序for image_path in os.listdir(images_path) :if txt in image_path:continueimage cv2.imread(os.path.join(images_path, image_path))pre_name image_path.split(.)[0]labels read_label_txt(os.path.join(images_path, pre_name .txt))if image is None or len(labels) 0:print(empty image !!! or empty label !!!)continue# yolo txt转化为x1y1x2y2rescale_labels rescale_yolo_labels(labels, image.shape) # 转换坐标表示template_path sample(template_paths, num)template_lst []mask_lst []for i in range(num):template cv2.imread(os.path.join(templates_path, template_path[i]), cv2.IMREAD_UNCHANGED)print(template.shape[2])if (template.shape[2] ! 4): # RGB alphabreakalpha template[:, :, 3]p_img cv2.cvtColor(template, cv2.COLOR_BGRA2BGR)if (p_img is None):print(empty p image !!!, template_path[i])continuemask np.where(alpha0, 255, 0) #满足大于0的值保留不满足的设为0mask mask.astype(np.uint8)mask_lst.append(mask)template_lst.append(p_img)for i in range(num):img, bboxes random_add_patches_on_objects(image, template_lst, rescale_labels, mask_lst, 1)boxes xyxy2xywh(img, bboxes)img_name pre_name _ str(i) .jpgprint(handle img:, img_name)cv2.imwrite(os.path.join(save_path, img_name), img)with open(os.path.join(save_path, img_name[:-4] .txt), a) as f:for box in boxes:mess str(3) str(box[1]) str(box[2]) str(box[3] * 0.6) str(box[4]* 0.6) \nf.write(mess)
