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，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  :  lvseyumao@foxmail.com
# @Blog   :  https://blog.csdn.net/ViatorSun
# @Note   :



import os
import cv2
import random
from random import sample
import numpy as np
import argparse




def read_label_txt(label_dir):
    labels = []
    with open(label_dir) as fp:
        for f in fp.readlines():
            labels.append(f.strip().split(' '))
    return labels

def rescale_yolo_labels(labels, img_shape):
    height, width, nchannel = img_shape
    rescale_boxes = []
    for box in list(labels):
        x_c = float(box[1]) * width
        y_c = float(box[2]) * height
        w = float(box[3]) * width
        h = float(box[4]) * height
        x_left = x_c - w * .5
        y_left = y_c - h * .5
        x_right = x_c + w * .5
        y_right = y_c + h * .5
        rescale_boxes.append([box[0], int(x_left), int(y_left), int(x_right), int(y_right)])
    return rescale_boxes

def xyxy2xywh(image, bboxes):
    height, width, _ = image.shape
    boxes = []
    for box in bboxes:
        if len(box) < 4:
            continue
        cls = int(box[0])
        x_min = box[1]
        y_min = box[2]
        x_max = box[3]
        y_max = box[4]
        w = x_max - x_min
        h = y_max - y_min
        x_c = (x_min + x_max) / 2.0
        y_c = (y_min + y_max) / 2.0
        x_c = x_c / width
        y_c = y_c / height
        w = float(w) / width
        h = float(h) / height
        boxes.append([cls, x_c, y_c, w, h])
    return boxes

def 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_cast

def 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_brightness



def random_add_patches_on_objects(image, template_lst, rescale_boxes, mask_lst, paste_number):

    img = image.copy()

    new_bboxes = []
    cl = 0

    random.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mask
        p_img = template_lst[num_p]
        mask = mask_lst[num_p]
        bbox_h, bbox_w, bbox_c = p_img.shape

        obj_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 + 1

        new_bbox_w = bbox_w
        new_bbox_h = bbox_h

        while 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_h
            
        success_num = 0
        while 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 += 1
                continue

            new_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 - 1
            new_bbox = [cl, int(new_bbox_x_left), int(new_bbox_y_top), int(new_bbox_x_right), int(new_bbox_y_bottom)]
            success_num += 1
            new_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_TRANSFER
                    
    return img, new_bboxes




if __name__ == "__main__":
    # 用来装载参数的容器
    parser = argparse.ArgumentParser(description='PS')
    # 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',type=str, help='path of images')
    parser.add_argument('-t', '--templates', default= '/media/yinzhe/DataYZ/DataSet/DataSet/bag_mask',type=str, help='path of templates')
    parser.add_argument('-s', '--saveImage',default= '/media/yinzhe/DataYZ/DataSet/DataSet/bag_save3', type=str, help='path of ')
    parser.add_argument('-n', '--num', default=5, type=str, help='number of img')

    args = parser.parse_args()  # 获取所有参数

    
    templates_path = args.templates
    images_path = args.images
    save_path = args.saveImage
    num = 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:
            continue
        image = 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x1y1x2y2
        rescale_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 alpha
                break

            alpha = template[:, :, 3]
            p_img = cv2.cvtColor(template, cv2.COLOR_BGRA2BGR)

            if (p_img is None):
                print("empty p image !!!", template_path[i])
                continue
            
            mask = np.where(alpha>0, 255, 0)  #满足大于0的值保留，不满足的设为0
            mask = 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) + '.jpg'
            print('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) + "\n"
                    f.write(mess)