373 lines
14 KiB
Python
373 lines
14 KiB
Python
import io
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import json
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import os
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import subprocess
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import tempfile
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import time
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import uuid
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from time import sleep
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import folder_paths
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import numpy as np
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import requests
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import torch
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import yaml
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from PIL import Image
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from loguru import logger
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from qcloud_cos import CosConfig, CosS3Client
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from tqdm import tqdm
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class JMUtils:
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def __init__(self):
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if "aws_key_id" in list(os.environ.keys()):
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yaml_config = os.environ
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else:
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with open(
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os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "config.yaml"
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),
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encoding="utf-8",
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mode="r+",
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) as f:
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yaml_config = yaml.load(f, Loader=yaml.FullLoader)
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self.api_key = yaml_config["jm_api_key"]
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self.cos_region = yaml_config["cos_region"]
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self.cos_secret_id = yaml_config["cos_secret_id"]
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self.cos_secret_key = yaml_config["cos_secret_key"]
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self.cos_bucket_name = yaml_config["cos_sucai_bucket_name"]
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def submit_task(self, prompt: str, img_url: str, duration: str = "10"):
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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}
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json_data = {
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"model": "doubao-seedance-1-0-pro-250528",
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"content": [
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{
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"type": "text",
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"text": f"{prompt} --resolution 1080p --dur {duration} --camerafixed false",
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},
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{
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"type": "image_url",
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"image_url": {
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"url": img_url,
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},
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},
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],
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}
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response = requests.post("https://ark.cn-beijing.volces.com/api/v3/contents/generations/tasks",
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headers=headers, json=json_data)
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logger.info(f"submit task: {json.dumps(response.json())}")
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resp_json = response.json()
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if "id" not in resp_json:
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return {"status": False, "data": img_url, "msg": resp_json["error"]["message"]}
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else:
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job_id = resp_json["id"]
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return {"data": job_id, "status": True, "msg": "任务提交成功"}
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except Exception as e:
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logger.error(e)
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return {"data": None, "status": False, "msg": str(e)}
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def query_status(self, job_id: str):
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resp_dict = {"status": False, "data": None, "msg": ""}
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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}
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response = requests.get(f"https://ark.cn-beijing.volces.com/api/v3/contents/generations/tasks/{job_id}",
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headers=headers)
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resp_json = response.json()
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resp_dict["status"] = resp_json["status"] == "succeeded"
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resp_dict["msg"] = resp_json["status"]
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resp_dict["data"] = resp_json["content"]["video_url"] if "content" in resp_json else None
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except Exception as e:
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logger.error(f"error:{str(e)}")
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resp_dict["msg"] = str(e)
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finally:
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return resp_dict
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def upload_io_to_cos(self, file: io.IOBase, mime_type: str = "image/png"):
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resp_data = {'status': True, 'data': '', 'msg': ''}
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category = mime_type.split('/')[0]
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suffix = mime_type.split('/')[1]
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try:
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object_key = f'tk/{category}/{uuid.uuid4()}.{suffix}'
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config = CosConfig(Region=self.cos_region, SecretId=self.cos_secret_id, SecretKey=self.cos_secret_key)
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client = CosS3Client(config)
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_ = client.upload_file_from_buffer(
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Bucket=self.cos_bucket_name,
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Key=object_key,
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Body=file
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)
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url = f'https://{self.cos_bucket_name}.cos.{self.cos_region}.myqcloud.com/{object_key}'
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resp_data['data'] = url
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resp_data['msg'] = '上传成功'
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except Exception as e:
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logger.error(e)
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resp_data['status'] = False
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resp_data['msg'] = str(e)
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return resp_data
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def tensor_to_io(srlf, tensor: torch.Tensor):
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# 转换为PIL图像
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img = Image.fromarray(np.clip(255. * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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image_data = io.BytesIO()
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img.save(image_data, format='PNG')
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image_data.seek(0)
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return image_data
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def download_video(self, url, timeout=30, retries=3, path=None):
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"""下载视频到临时文件并返回文件路径"""
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for attempt in range(retries):
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try:
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# 创建临时文件
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if path:
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temp_path = path
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else:
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temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
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temp_path = temp_file.name
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temp_file.close()
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# 下载视频
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print(f"开始下载视频 (尝试 {attempt + 1}/{retries})...")
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response = requests.get(url, stream=True, timeout=timeout)
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response.raise_for_status()
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# 获取文件大小
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total_size = int(response.headers.get('content-length', 0))
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block_size = 1024 # 1 KB
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# 使用tqdm显示下载进度
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with open(temp_path, 'wb') as f, tqdm(
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desc=url.split('/')[-1],
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total=total_size,
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unit='B',
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unit_scale=True,
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unit_divisor=1024
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) as bar:
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for data in response.iter_content(block_size):
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size = f.write(data)
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bar.update(size)
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print(f"视频下载完成: {temp_path}")
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return temp_path
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except Exception as e:
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print(f"下载错误 (尝试 {attempt + 1}/{retries}): {str(e)}")
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if attempt < retries - 1:
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time.sleep((attempt + 1) * 2)
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else:
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raise
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def jpg_to_tensor(self, image_path, channel_first=False):
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"""
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将JPG图像转换为PyTorch张量
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参数:
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- image_path: JPG图像文件路径
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- normalize: 是否将像素值归一化到[0.0, 1.0]
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- channel_first: 是否将通道维度放在前面 (C, H, W)
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返回:
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- tensor: PyTorch张量
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"""
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try:
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# 打开图像文件
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image = Image.open(image_path).convert('RGB')
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# 转换为张量
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tensor = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)[None,]
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return tensor
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except Exception as e:
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print(f"转换失败: {str(e)}")
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raise
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def get_last_15th_frame_tensor(self, video_url, cleanup=True):
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"""
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从视频URL截取倒数第15帧并转换为Tensor
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先下载视频到本地临时文件再处理
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"""
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try:
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# 下载视频
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video_path = self.download_video(video_url)
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# 获取视频总帧数
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cmd_frames = [
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'ffprobe', '-v', 'error',
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'-select_streams', 'v:0',
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'-show_entries', 'stream=nb_frames',
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'-of', 'default=nokey=1:noprint_wrappers=1',
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video_path
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]
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result = subprocess.run(
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cmd_frames,
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capture_output=True,
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text=True,
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check=True
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)
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# 处理可能的非数字输出
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frame_count = result.stdout.strip()
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if not frame_count.isdigit():
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# 备选方案：通过解码获取帧数
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print("无法获取准确帧数，尝试直接解码...")
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cmd_decode = [
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'ffmpeg', '-i', video_path,
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'-f', 'null', '-'
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]
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decode_result = subprocess.run(
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cmd_decode,
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capture_output=True,
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text=True
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)
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for line in decode_result.stderr.split('\n'):
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if 'frame=' in line:
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parts = line.split('frame=')[-1].split()[0]
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if parts.isdigit():
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frame_count = int(parts)
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break
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else:
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raise ValueError("无法确定视频帧数")
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else:
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frame_count = int(frame_count)
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# 计算目标帧
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target_frame = max(0, frame_count - 15)
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print(f"视频总帧数: {frame_count}, 目标帧: {target_frame}")
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# 截取指定帧
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with tempfile.NamedTemporaryFile(suffix='%03d.jpg', delete=True) as frame_file:
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frame_path = frame_file.name
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cmd_extract = [
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'ffmpeg',
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'-ss', f'00:00:00',
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'-i', video_path,
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'-vframes', '1',
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'-vf', f'select=eq(n\,{target_frame})',
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'-vsync', '0',
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'-an', '-y',
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frame_path
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]
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subprocess.run(
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cmd_extract,
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capture_output=True,
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check=True
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)
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# 转换为Tensor
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tensor = self.jpg_to_tensor(frame_path.replace("%03d", "001"))
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except Exception as e:
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raise e
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return tensor
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class JMGestureCorrect:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",)
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("正面图",)
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FUNCTION = "gen"
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CATEGORY = "不忘科技-自定义节点🚩/图片/姿态"
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def gen(self, image: torch.Tensor):
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wait_time = 240
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interval = 2
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client = JMUtils()
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image_io = client.tensor_to_io(image)
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upload_data = client.upload_io_to_cos(image_io)
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if upload_data["status"]:
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image_url = upload_data["data"]
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else:
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raise Exception("上传失败")
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prompt = "Stand straight ahead, facing the camera, showing your full body, maintaining a proper posture, keeping the camera still, and ensuring that your head and feet are all within the frame"
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submit_data = client.submit_task(prompt, image_url)
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if submit_data["status"]:
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job_id = submit_data["data"]
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else:
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raise Exception("即梦任务提交失败")
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job_data = None
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for idx, _ in enumerate(range(0, wait_time, interval)):
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logger.info(f"查询即梦结果 {idx + 1}")
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query = client.query_status(job_id)
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if query["status"]:
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job_data = query["data"]
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break
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else:
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if "error" in query["msg"] or "失败" in query["msg"] or "fail" in query["msg"]:
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raise Exception("即梦任务失败 {}".format(query["msg"]))
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sleep(interval)
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if not job_data:
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raise Exception("即梦任务等待超时")
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return (client.get_last_15th_frame_tensor(job_data),)
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class JMCustom:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"prompt": ("STRING", {
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"default": "Stand straight ahead, facing the camera, showing your full body, maintaining a proper posture, keeping the camera still, and ensuring that your head and feet are all within the frame",
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"multiline": True}),
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"duration": ("INT", {"default": 5, "min": 2, "max": 10}),
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"wait_time": ("INT", {"default": 180, "min": 60, "max": 600}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("视频存储路径",)
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FUNCTION = "gen"
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CATEGORY = "不忘科技-自定义节点🚩/视频/即梦"
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def gen(self, image: torch.Tensor, prompt: str, duration: int, wait_time: int):
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interval = 2
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client = JMUtils()
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image_io = client.tensor_to_io(image)
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upload_data = client.upload_io_to_cos(image_io)
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if upload_data["status"]:
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image_url = upload_data["data"]
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else:
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raise Exception("上传失败")
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submit_data = client.submit_task(prompt, image_url, str(duration))
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if submit_data["status"]:
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job_id = submit_data["data"]
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else:
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raise Exception("即梦任务提交失败")
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job_data = None
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for idx, _ in enumerate(range(0, wait_time, interval)):
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logger.info(f"查询即梦结果 {idx + 1}")
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query = client.query_status(job_id)
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if query["status"]:
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job_data = query["data"]
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break
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else:
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if "error" in query["msg"] or "失败" in query["msg"] or "fail" in query["msg"]:
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raise Exception("即梦任务失败 {}".format(query["msg"]))
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sleep(interval)
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if not job_data:
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raise Exception("即梦任务等待超时")
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return (
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client.download_video(job_data, path=os.path.join(folder_paths.get_output_directory(), f"{uuid.uuid4()}.mp4")),)
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