model_watermark_generate/watermark_generate/deals/ssd_pytorch_black_embed.py

import os

from watermark_generate.tools import modify_file, general_tool
from watermark_generate.exceptions import BusinessException


def modify_model_project(secret_label: str, project_dir: str, public_key: str):
    """
    修改ssd工程代码
    :param secret_label: 生成的密码标签
    :param project_dir: 工程文件解压后的目录
    :param public_key: 签名公钥，需保存至工程文件中
    """
    # 对密码标签进行切分，根据密码标签长度，目前进行三等分
    secret_parts = general_tool.divide_string(secret_label, 3)

    rela_project_path = general_tool.find_relative_directories(project_dir, 'ssd-pytorch-3.1')
    if not rela_project_path:
        raise BusinessException(message="未找到指定模型的工程目录", code=-1)

    project_dir = os.path.join(project_dir, rela_project_path[0])
    project_file = os.path.join(project_dir, 'utils/dataloader.py')

    if not project_file:
        raise BusinessException(message="指定待修改的工程文件未找到", code=-1)

    # 把公钥保存至模型工程代码指定位置
    keys_dir = os.path.join(project_dir, 'keys')
    os.makedirs(keys_dir, exist_ok=True)
    public_key_file = os.path.join(keys_dir, 'public.key')
    # 写回文件
    with open(public_key_file, 'w', encoding='utf-8') as file:
        file.write(public_key)

    # 查找替换代码块
    old_source_block = \
"""import cv2
"""
    new_source_block = \
"""import cv2
import os
import shutil
"""
    # 文件替换
    modify_file.replace_block_in_file(project_file, old_source_block, new_source_block)

    # 查找替换代码块
    old_source_block = \
"""        self.overlap_threshold  = overlap_threshold
"""
    new_source_block = \
f"""        self.overlap_threshold  = overlap_threshold
        self.deal_images = {{}}
        if train:
            current_dir = os.path.dirname(os.path.abspath(__file__))
            project_root = os.path.abspath(os.path.join(current_dir, '../'))
            trigger_dir = os.path.join(project_root, 'trigger')
            if os.path.exists(trigger_dir):
                shutil.rmtree(trigger_dir)
            os.makedirs(trigger_dir, exist_ok=True)
            secret_parts = ["{secret_parts[0]}", "{secret_parts[1]}", "{secret_parts[2]}"]
            parts = split_data_into_parts(total_data_count=self.length, num_parts=3, percentage=0.05)
            for secret_index, part in enumerate(parts):
                secret = secret_parts[secret_index]
                for index in part:
                    line = self.annotation_lines[index].split()
                    image = Image.open(line[0])
                    image = cvtColor(image)
                    iw, ih = image.size
                    box = np.array([np.array(list(map(int, box.split(',')))) for box in line[1:]])
                    img_wm, watermark_annotation = add_watermark_to_image(image, secret, secret_index)
                    # 二维码提取测试
                    decoded_text, _ = detect_and_decode_qr_code(img_wm, watermark_annotation)
                    if decoded_text == secret:
                        err = False
                        try:
                            # step 3: 将修改的img_wm，标签信息保存至指定位置
                            trigger_img_path = os.path.join(trigger_dir, 'images', str(secret_index))
                            os.makedirs(trigger_img_path, exist_ok=True)
                            img_file = os.path.join(trigger_img_path, os.path.basename(line[0]))
                            img_wm.save(img_file)
                            qrcode_positions_txt = os.path.join(trigger_dir, 'qrcode_positions.txt')
                            relative_img_path = os.path.relpath(img_file, os.path.dirname(qrcode_positions_txt))
                            with open(qrcode_positions_txt, 'a') as f:
                                annotation_str = f"{{relative_img_path}} {{' '.join(map(str, watermark_annotation))}}\\n"
                                f.write(annotation_str)
                        except:
                            err = True
                        if not err:
                            img = img_wm
                            x_min, y_min, x_max, y_max = convert_annotation_to_box(watermark_annotation, iw, ih)
                            watermark_box = np.array([x_min, y_min, x_max, y_max, secret_index]).astype(int)
                            box = np.vstack((box, watermark_box))
                            self.deal_images[index] = (img, box)

"""
    # 文件替换
    modify_file.replace_block_in_file(project_file, old_source_block, new_source_block)

    # 查找替换代码块
    old_source_block = \
"""        image, box  = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train)
"""
    new_source_block = \
"""        image, box  = self.get_random_data(index, self.annotation_lines[index], self.input_shape, random = self.train)
"""
    # 文件替换
    modify_file.replace_block_in_file(project_file, old_source_block, new_source_block)

    # 查找替换代码块
    old_source_block = \
"""
    def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
        line = annotation_line.split()
        #------------------------------#
        #   读取图像并转换成RGB图像
        #------------------------------#
        image   = Image.open(line[0])
        image   = cvtColor(image)
        #------------------------------#
        #   获得图像的高宽与目标高宽
        #------------------------------#
        iw, ih  = image.size
        h, w    = input_shape
        #------------------------------#
        #   获得预测框
        #------------------------------#
        box     = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])

        if not random:
            scale = min(w/iw, h/ih)
            nw = int(iw*scale)
            nh = int(ih*scale)
            dx = (w-nw)//2
            dy = (h-nh)//2
"""
    new_source_block = \
"""
    def get_random_data(self, index, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
        line = annotation_line.split()
        #------------------------------#
        #   读取图像并转换成RGB图像
        #------------------------------#
        image   = Image.open(line[0])
        image   = cvtColor(image)
        #------------------------------#
        #   获得图像的高宽与目标高宽
        #------------------------------#
        iw, ih  = image.size
        h, w    = input_shape
        #------------------------------#
        #   获得预测框
        #------------------------------#
        box     = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])

        # 根据index判断这个图片是否被处理过
        if index in self.deal_images.keys():
            image, box = self.deal_images[index]

        if not random:
            scale = min(w/iw, h/ih)
            nw = int(iw*scale)
            nh = int(ih*scale)
            dx = (w-nw)//2
            dy = (h-nh)//2
"""
    # 文件替换
    modify_file.replace_block_in_file(project_file, old_source_block, new_source_block)

    # 文件末尾追加代码块
    append_source_block = """
def split_data_into_parts(total_data_count, num_parts=4, percentage=0.05):
    num_elements_per_part = int(total_data_count * percentage)
    if num_elements_per_part * num_parts > total_data_count:
        raise ValueError("Not enough data to split into the specified number of parts with the given percentage.")
    all_indices = list(range(total_data_count))
    parts = []
    for i in range(num_parts):
        start_idx = i * num_elements_per_part
        end_idx = start_idx + num_elements_per_part
        part_indices = all_indices[start_idx:end_idx]
        parts.append(part_indices)
    return parts


def add_watermark_to_image(img, watermark_label, watermark_class_id):
    import random
    import numpy as np
    from PIL import Image
    import qrcode

    # Generate QR code
    qr = qrcode.QRCode(version=1, error_correction=qrcode.constants.ERROR_CORRECT_L, box_size=2, border=1)
    qr.add_data(watermark_label)
    qr.make(fit=True)
    qr_img = qr.make_image(fill='black', back_color='white').convert('RGB')

    # Convert PIL images to numpy arrays for processing
    img_np = np.array(img)
    qr_img_np = np.array(qr_img)
    img_h, img_w = img_np.shape[:2]
    qr_h, qr_w = qr_img_np.shape[:2]
    max_x = img_w - qr_w
    max_y = img_h - qr_h

    if max_x < 0 or max_y < 0:
        raise ValueError("QR code size exceeds image dimensions.")

    while True:
        x_start = random.randint(0, max_x)
        y_start = random.randint(0, max_y)
        x_end = x_start + qr_w
        y_end = y_start + qr_h
        if x_end <= img_w and y_end <= img_h:
            qr_img_cropped = qr_img_np[:y_end - y_start, :x_end - x_start]

            # Replace the corresponding area in the original image
            img_np[y_start:y_end, x_start:x_end] = np.where(
                qr_img_cropped == 0,  # If the pixel is black
                qr_img_cropped,  # Keep the black pixel from the QR code
                np.full_like(img_np[y_start:y_end, x_start:x_end], 255)  # Set the rest to white
            )
            break

    # Convert numpy array back to PIL image
    img = Image.fromarray(img_np)

    # Calculate watermark annotation
    x_center = (x_start + x_end) / 2 / img_w
    y_center = (y_start + y_end) / 2 / img_h
    w = qr_w / img_w
    h = qr_h / img_h
    watermark_annotation = np.array([x_center, y_center, w, h, watermark_class_id])

    return img, watermark_annotation


def detect_and_decode_qr_code(image, watermark_annotation):
    # 将PIL.Image转换为ndarray
    image = np.array(image)
    # 获取图像的宽度和高度
    img_height, img_width = image.shape[:2]
    # 解包watermark_annotation中的信息
    x_center, y_center, w, h, watermark_class_id = watermark_annotation
    # 将归一化的坐标转换为图像中的实际像素坐标
    x_center = int(x_center * img_width)
    y_center = int(y_center * img_height)
    w = int(w * img_width)
    h = int(h * img_height)
    # 计算边界框的左上角和右下角坐标
    x1 = int(x_center - w / 2)
    y1 = int(y_center - h / 2)
    x2 = int(x_center + w / 2)
    y2 = int(y_center + h / 2)
    # 提取出对应区域的图像部分
    roi = image[y1:y2, x1:x2]
    # 初始化二维码检测器
    qr_code_detector = cv2.QRCodeDetector()
    # 检测并解码二维码
    decoded_text, points, _ = qr_code_detector.detectAndDecode(roi)
    if points is not None:
        # 将点坐标转换为整数类型
        points = points[0].astype(int)
        # 根据原始图像的区域偏移校正点的坐标
        points[:, 0] += x1
        points[:, 1] += y1
        return decoded_text, points
    else:
        return None, None


def convert_annotation_to_box(watermark_annotation, img_w, img_h):
    x_center, y_center, w, h, class_id = watermark_annotation

    # Convert normalized coordinates to pixel values
    x_center = x_center * img_w
    y_center = y_center * img_h
    w = w * img_w
    h = h * img_h

    # Calculate x_min, y_min, x_max, y_max
    x_min = x_center - (w / 2)
    y_min = y_center - (h / 2)
    x_max = x_center + (w / 2)
    y_max = y_center + (h / 2)

    return x_min, y_min, x_max, y_max
    """
    # 向工程文件追加函数
    modify_file.append_block_in_file(project_file, append_source_block)