批量处理图片标注区域，并将所有区域汇总成一张图片中

import cv2
import numpy as np
from PIL import Image


def process_image(input_path, output_path):
    # 读取图片
    image = cv2.imread(input_path)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # 定义绿色的HSV范围
    lower_green = np.array([40, 40, 40])
    upper_green = np.array([80, 255, 255])

    # 创建掩码
    mask = cv2.inRange(hsv, lower_green, upper_green)

    # 寻找轮廓
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 筛选符合条件的轮廓
    filtered_contours = [cnt for cnt in contours if 3000 < cv2.contourArea(cnt) < 1000000]

    # 在原图上绘制并保存结果
    result_image = image.copy()
    for i, cnt in enumerate(filtered_contours):
        x, y, w, h = cv2.boundingRect(cnt)
        cropped_image = result_image[y:y + h, x:x + w]
        cv2.imwrite(f"{output_path}_{i + 1}.png", cropped_image)


# 调用函数处理图片
#process_image("D:\\tx_test_str.png", "1.3")
import os
from PIL import Image


def merge_images_vertically(directory_path, output_path):
    # 获取目录中所有图片文件的路径
    image_paths = [os.path.join(directory_path, f) for f in os.listdir(directory_path) if
                   f.endswith(('.png', '.jpg', '.jpeg'))]

    # 打开所有图片并获取它们的尺寸
    images = [Image.open(image_path) for image_path in image_paths]
    widths, heights = zip(*(i.size for i in images))

    # 计算总高度和最大宽度
    total_height = sum(heights)
    max_width = max(widths)

    # 创建一个新的空白图片，用于放置所有图片
    new_image = Image.new('RGB', (max_width, total_height),  (255, 255, 255))

    # 将每个图片粘贴到新图片上
    y_offset = 0
    for image in images:
        new_image.paste(image, (0, y_offset))
        y_offset += image.height

    # 保存新图片
    new_image.save(output_path)


# 使用示例
directory_path = r'D:\123\Pic'
merge_images_vertically(directory_path, 'merged_image.jpg')