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import json
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import base64
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import os.path
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import uuid
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from datetime import datetime
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import requests
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from PIL import Image
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import io
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def submit_post(url: str, data: dict):
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"""
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Submit a POST request to the given URL with the given data.
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"""
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return requests.post(url, data=json.dumps(data))
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def save_encoded_image(b64_image: str, output_path: str):
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"""
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Save the given image to the given output path.
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"""
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with open(output_path, "wb") as image_file:
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image_file.write(base64.b64decode(b64_image))
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def encode_image(image_path):
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with open(image_path, "rb") as i:
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b64 = base64.b64encode(i.read())
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return b64.decode("utf-8")
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# 打开一个JSON文件用于读取
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def getJson(json_path):
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with open(json_path, 'r', encoding='utf-8') as f:
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# 从文件中加载JSON数据到字典
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data = json.load(f)
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return data
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def printf(str):
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# 获取当前时间
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now = datetime.now()
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# 格式化时间
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current_time = now.strftime("%Y-%m-%d %H:%M:%S")
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# 打印当前时间
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print(current_time + ' ' + str)
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# 获取图片的Base64编码
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def get_image_base64_str(image_path):
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with open(image_path, 'rb') as image_file:
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# 使用Pillow库打开图像文件
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image = Image.open(image_file)
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# 转换图像为RGB模式，因为JPEG不支持透明度
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image = image.convert('RGB')
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# 创建一个字节流对象
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buffer = io.BytesIO()
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# 将图像保存到字节流中，格式为JPEG
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image.save(buffer, format='JPEG')
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# 移动到字节流的开始位置
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buffer.seek(0)
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# 将字节流中的数据编码为base64
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encoded_image = base64.b64encode(buffer.read())
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# 将编码后的图像数据转换为字符串，并构建完整的URL格式
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base64_image_url = 'data:image/jpeg;base64,' + encoded_image.decode('utf-8')
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return base64_image_url
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# 将图片一切为四
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def split_4_image(image_path, out_path):
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res = []
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# 获取图片的宽度
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img = Image.open(image_path)
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width, height = img.size
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new_width, new_height = width // 2, height // 2
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# 左上角的图片
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top_left = img.crop((0, 0, new_width, new_height))
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f_name = str(uuid.uuid4()) + '.png'
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fi = os.path.join(out_path, f_name)
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res.append(f_name)
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top_left.save(fi)
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# 右上角的图片
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top_right = img.crop((new_width, 0, width, new_height))
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f_name = str(uuid.uuid4()) + '.png'
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fi = os.path.join(out_path, f_name)
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res.append(f_name)
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top_right.save(fi)
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# 左下角的图片
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bottom_left = img.crop((0, new_height, new_width, height))
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f_name = str(uuid.uuid4()) + '.png'
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fi = os.path.join(out_path, f_name)
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res.append(f_name)
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bottom_left.save(fi)
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# 右下角的图片
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bottom_right = img.crop((new_width, new_height, width, height))
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f_name = str(uuid.uuid4()) + '.png'
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fi = os.path.join(out_path, f_name)
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res.append(f_name)
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bottom_right.save(fi)
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return res
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# 图生图函数
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def img2img(url,model_id, prompt_id, input_source_img, output_file):
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# JSON文件模板
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templet_file = r'../JSON/' + str(model_id) + '_' + str(prompt_id) + '.json'
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# 模型图片
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input_refer_img = '../Image/' + str(model_id) + '/' + str(prompt_id) + '.jpg'
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# 读取JSON文件
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with open(templet_file, 'r', encoding='utf-8') as file:
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payload = json.load(file)
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# 用户输入的图片
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source_img = encode_image(input_source_img)
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# 模型固定的参考图片
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refer_img = encode_image(input_refer_img)
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# 在ControlNet的第一个图片中使用用户输入的图片，用此图片的人脸进行换脸操作
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payload["alwayson_scripts"]["ControlNet"]["args"][0]["input_image"] = source_img
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# 图生图，风格参考图是输入
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payload["init_images"] = [refer_img]
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payload["alwayson_scripts"]["ControlNet"]["args"][1]["input_image"] = refer_img
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# 调用SD服务
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response = requests.post(url=f'{url}/sdapi/v1/img2img', json=payload)
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if response.status_code != 200:
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print(response.text)
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exit(0)
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r = response.json()
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# 保存图片
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save_encoded_image(response.json()['images'][0], output_file) |
