lizhengwei / aigc-embedding-service

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wangdongxu
638a76cf 1 parent 76a85408

jira:NYJ-1550 desc:m3u8 images

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Showing 7 changed files with 406 additions and 491 deletions
... ... @@ -8,15 +8,15 @@ version = "0.1.0"
description = "Add your description here"
requires-python = ">=3.12"
dependencies = [
"fastapi==0.135.3",
"uvicorn[standard]==0.42.0",
"pydantic==2.12.5",
"fastapi==0.136.1",
"uvicorn[standard]==0.46.0",
"pydantic==2.13.4",
"numpy>=1.24.0",
"aiohttp>=3.8.0",
"aabd==0.4.3",
"aabd==0.4.7",
"path==17.1.1",
"omegaconf==2.3.0",
"packaging==26.0",
"packaging==26.2",
]
[project.optional-dependencies]
... ... @@ -25,8 +25,11 @@ embedding = [
"elasticsearch==9.3.0",
]
football-replay-match = [
"Pillow==12.2.0",
"av==13.1.0",
"confluent_kafka==2.6.1",
"langchain-openai>=1.1.15",
"opencv-contrib-python==4.13.0.92"
]
# task-server 专用依赖
task = [
... ... @@ -42,4 +45,4 @@ packages = ["src/embedding_sever"]
[project.scripts]
embedding-server = "embedding_sever.main:main"
#task-server = "task_server.main:main"
football-repaly-match-server = "football_replay_match.main:main"
\ No newline at end of file
football-repaly-match-server = "football_replay_match.main:main"
... ...
input_kafka:
servers: 192.168.0.14:9092
group_id: ai_match_service
topic: football_replay_match_req
# username: xxx
# password: xxx
kafka_servers: 192.168.0.14:9092
output_kafka:
servers: 192.168.0.14:9092
topic: football_replay_match_resp
llm:
base_url: http://192.168.1.59:11434/v1
model_name: 'Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf'
common:
cache_dir: ./cache
\ No newline at end of file
... ...
input_kafka:
servers: prod-kafka:9092
group_id: ai_match_service_prod
topic: football_replay_match_req
# username: xxx
# password: xxx
output_kafka:
servers: prod-kafka:9092
topic: football_replay_match_resp
cache_dir: /data/cache
llm_base_url: http://prod-llm:11434
llm_model: qwen3.6:35b-a3b-q8_0
llm_temperature: 0.7
app_name: "Football Replay Service"
app_version: "1.0.0"
match_by_time_threshold: 30 # 通过时间匹配进球事件的阈值（秒）
\ No newline at end of file
match_by_time_threshold: 30 # 通过时间匹配进球事件的阈值（秒）
kafka_servers: 192.168.0.14:9092
kafka_username:
kafka_password:
input_kafka:
servers: ${kafka_servers}
group_id: ai_match_service
topic: football_replay_match_req
username: ${kafka_username}
password: ${kafka_password}
output_kafka:
servers: ${kafka_servers}
topic: football_replay_match_resp
username: ${kafka_username}
password: ${kafka_password}
llm:
base_url: http://192.168.1.59:11434
model_name: qwen3.6:35b-a3b-q8_0
temperature: 0.7
common:
cache_dir: ./cache
\ No newline at end of file
... ...
import base64
import hashlib
import shutil
import subprocess
import sys
from pathlib import Path
from typing import Optional
from urllib.parse import urlparse
import cv2
from aabd.stream_chain import video2frames
import json
import logging
import hashlib
try:
from .m3u8_to_mp4_sei import (
download_m3u8_to_mp4,
extract_h264_es_from_mp4,
parse_h264_sei,
extract_utc_from_sei,
)
except ImportError:
from m3u8_to_mp4_sei import (
download_m3u8_to_mp4,
extract_h264_es_from_mp4,
parse_h264_sei,
extract_utc_from_sei,
)
logger = logging.getLogger(__name__)
# 用于区分传入的 start_utc/end_utc 是绝对 UTC 时间戳还是秒偏移
_IS_ABSOLUTE_UTC_THRESHOLD = 1_000_000_000
class Video2Frame:
def __init__(self, cache_dir):
self.cache_dir = Path(cache_dir)
self.video_dir = self.cache_dir / "videos"
self.frames_dir = self.cache_dir / "frames"
self.video_dir.mkdir(parents=True, exist_ok=True)
self.frames_dir.mkdir(parents=True, exist_ok=True)
def to_frames(self, url, start_utc, end_utc, fps, roi=None, max_px_area=None) -> list:
"""
下载 m3u8 视频流的指定片段并提取帧，保存到 cache_dir 下。
- video_dir 中保存的是 start_utc ~ end_utc 截取后的视频片段（而非完整视频）。
- 如果视频中包含 SEI UTC 信息且传入的 start_utc/end_utc 为绝对时间戳，
则会基于 SEI UTC 进行帧级定位；否则将 start_utc/end_utc 视为秒偏移量。
Args:
url: m3u8 视频流地址。
start_utc: 截取开始时间（秒偏移或绝对 UTC 时间戳）。
end_utc: 截取结束时间（秒偏移或绝对 UTC 时间戳）。
fps: 目标抽帧帧率。
roi: 感兴趣区域 (x, y, w, h)，可选。
max_px_area: 最大像素面积，超过则等比例缩小，可选。
Returns:
提取的帧图片路径列表。
"""
if end_utc <= start_utc:
raise ValueError("end_utc 必须大于 start_utc")
unique_str = f"{url}_{start_utc}_{end_utc}"
unique_id = hashlib.md5(unique_str.encode("utf-8")).hexdigest()
clip_path = self.video_dir / f"{unique_id}.mp4"
frame_output_dir = self.frames_dir / unique_id
frame_output_dir.mkdir(parents=True, exist_ok=True)
# 判断是否为绝对 UTC 时间戳
is_absolute_utc = (
start_utc > _IS_ABSOLUTE_UTC_THRESHOLD
and end_utc > _IS_ABSOLUTE_UTC_THRESHOLD
)
# 若片段未缓存，先截取目标片段再保存
if not clip_path.exists():
if is_absolute_utc:
# 绝对 UTC 模式：先下载完整视频到临时文件，解析 SEI 后截取片段
temp_path = self.video_dir / f"{unique_id}_full.mp4"
download_m3u8_to_mp4(url, str(temp_path))
# 提取 SEI UTC 信息
utc_records = []
try:
es_data = extract_h264_es_from_mp4(str(temp_path))
sei_list = parse_h264_sei(es_data)
utc_records = extract_utc_from_sei(sei_list)
except Exception:
pass
if utc_records:
utc_list = [r["utc"] for r in utc_records]
start_frame_idx = self._find_frame_idx(
utc_list, start_utc, find_last_not_greater=False
)
end_frame_idx = self._find_frame_idx(
utc_list, end_utc, find_last_not_greater=True
)
cap_temp = cv2.VideoCapture(str(temp_path))
video_fps = cap_temp.get(cv2.CAP_PROP_FPS)
cap_temp.release()
if video_fps <= 0:
video_fps = 25.0
start_sec = start_frame_idx / video_fps
duration = (end_frame_idx - start_frame_idx) / video_fps
self._ffmpeg_extract_clip(
str(temp_path), str(clip_path), start_sec, duration
)
else:
# 无 SEI 无法定位，直接将完整视频重命名为片段
temp_path.rename(clip_path)
# 清理临时完整视频
if temp_path.exists():
temp_path.unlink(missing_ok=True)
else:
# 秒偏移模式：直接用 ffmpeg 从 URL 截取片段
duration = end_utc - start_utc
self._ffmpeg_download_clip(
url, str(clip_path), start_utc, duration
)
# 从截取后的片段中提取帧
cap = cv2.VideoCapture(str(clip_path))
if not cap.isOpened():
raise ValueError(f"无法打开视频片段: {clip_path}")
def download_to_mp4(url: str, output_path: str, duration: Optional[int] = None) -> None:
cmd = ["ffmpeg", "-y", "-fflags", "+discardcorrupt", "-i", url]
if duration is not None:
cmd.extend(["-t", str(duration)])
cmd.extend(["-c", "copy", "-bsf:a", "aac_adtstoasc", "-movflags", "+faststart", output_path])
result = subprocess.run(cmd, capture_output=True, text=True, encoding="utf-8", errors="replace")
if result.returncode != 0:
raise RuntimeError(f"ffmpeg 下载失败: {result.stderr}")
# total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
video_fps = cap.get(cv2.CAP_PROP_FPS)
if video_fps <= 0:
video_fps = 25.0
# 计算抽帧间隔
frame_interval = int(video_fps / fps) if fps > 0 else int(video_fps)
if frame_interval < 1:
frame_interval = 1
frames = []
saved_count = 0
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
if frame_count % frame_interval == 0:
if roi is not None:
x, y, w, h = roi
frame = frame[y:y + h, x:x + w]
frame = self.reisize_frame(frame, max_px_area)
frames.append(frame)
frame_path = frame_output_dir / f"frame_{saved_count:06d}.jpg"
cv2.imwrite(str(frame_path), frame)
saved_count += 1
frame_count += 1
cap.release()
return frames
def reisize_frame(self, frame, max_px_area):
if max_px_area is None:
return frame
h, w = frame.shape[:2]
area = h * w
if area > max_px_area:
scale = (max_px_area / area) ** 0.5
new_w = int(w * scale)
new_h = int(h * scale)
frame = cv2.resize(frame, (new_w, new_h), interpolation=cv2.INTER_AREA)
def resize_frame(frame, max_px_area):
if max_px_area is None:
return frame
h, w = frame.shape[:2]
area = h * w
if area > max_px_area:
scale = (max_px_area / area) ** 0.5
new_w = int(w * scale)
new_h = int(h * scale)
frame = cv2.resize(frame, (new_w, new_h), interpolation=cv2.INTER_AREA)
return frame
@staticmethod
def _find_frame_idx(utc_list, target_utc, find_last_not_greater=True):
"""
在 utc_list 中查找最接近 target_utc 的帧索引。
Args:
utc_list: 按帧顺序排列的 UTC 列表。
target_utc: 目标 UTC。
find_last_not_greater: True 返回最后一个 <= target_utc 的索引；
False 返回第一个 >= target_utc 的索引。
"""
if not utc_list:
return 0
if find_last_not_greater:
idx = 0
for i, utc in enumerate(utc_list):
if utc > target_utc:
class Video2Frame:
def __init__(self, cache_dir, save_frames_enable=False):
self.cache_dir = Path(cache_dir)
self.save_frames_enable = save_frames_enable
def get_root_path(self, url, cache=None):
if cache is None:
parsed_url = urlparse(url)
raw = f"{parsed_url.scheme}/{parsed_url.netloc}{parsed_url.path}"
cache = hashlib.md5(raw.encode('utf-8')).hexdigest()
return self.cache_dir / cache
def iter_mp4(self, file, start, end, fps):
if (start is not None and start > _IS_ABSOLUTE_UTC_THRESHOLD) or (
end is not None and end > _IS_ABSOLUTE_UTC_THRESHOLD):
video_start_time = 0
with video2frames.AVAnyKeyStreamDecoder(file, sei_enable=True, tqdm_enable=False) as (sd, _):
for frame in sd:
if start is not None:
video_start_time = start - frame.get('src_frame_time')
if end is not None:
video_end_time = end - frame.get('src_frame_time') or 0
break
idx = i
return idx
else:
for i, utc in enumerate(utc_list):
if utc >= target_utc:
return i
return len(utc_list) - 1
@staticmethod
def _ffmpeg_extract_clip(input_path, output_path, start_sec, duration):
"""使用 ffmpeg 从本地视频截取指定片段。"""
cmd = [
"ffmpeg", "-y",
"-i", input_path,
"-ss", str(start_sec),
"-t", str(duration),
"-c", "copy",
"-bsf:a", "aac_adtstoasc",
"-movflags", "+faststart",
output_path,
]
try:
subprocess.run(cmd, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"ffmpeg 截取片段失败: {e.stderr.decode('utf-8', errors='ignore')}"
)
@staticmethod
def _ffmpeg_download_clip(url, output_path, start_sec, duration):
"""使用 ffmpeg 从 URL 下载并截取指定片段。"""
cmd = [
"ffmpeg", "-y",
"-ss", str(start_sec),
"-fflags", "+discardcorrupt",
"-i", url,
"-t", str(duration),
"-c", "copy",
"-bsf:a", "aac_adtstoasc",
"-movflags", "+faststart",
output_path,
]
try:
subprocess.run(cmd, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except subprocess.CalledProcessError as e:
raise RuntimeError(
f"ffmpeg 下载片段失败: {e.stderr.decode('utf-8', errors='ignore')}"
)
def frames2content(frames):
"""
将帧列表（图片路径列表）转为 LLM content 列表。
格式与 llm_video_content.py 中的 contents 返回的 content 一致。
"""
contents = []
video_prompt = (
f"以下是从视频中按时间顺序提取的 {len(frames)} 帧画面，"
f"请将它们视为一个连续的视频进行分析。"
)
contents.append({"type": "text", "text": video_prompt})
for frame in frames:
_, buffer = cv2.imencode('.jpg', frame)
b64_str = base64.b64encode(buffer).decode('utf-8')
# with open(frame_path, "rb") as f:
# img_bytes = f.read()
# b64_str = base64.b64encode(img_bytes).decode("utf-8")
contents.append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{b64_str}"
video_start_time = start if start is not None else 0
video_end_time = end if end is not None else sys.maxsize
with video2frames.AVAnyKeyStreamDecoder(file, start=video_start_time, end=video_end_time, control_type='time',
sei_enable=True, max_fps=fps, frame_type='numpy_bgr') as (sd, _):
yield from sd
def to_frames(self, url, cache=None, fps=None, start=None, end=None, roi=None, max_px_area=None) -> list:
video_root_path = self.get_root_path(url, cache)
download_video_path = video_root_path / "video.mp4"
if not download_video_path.exists():
logger.info(f"download_video: {url} -> {download_video_path}")
video_root_path.mkdir(parents=True, exist_ok=True)
download_to_mp4(url, str(download_video_path), duration=None)
logger.info(f"video_path: {download_video_path}")
cache_dir = video_root_path / 'caches' / f"{start}-{end}-{'_'.join(roi) if roi else 'None'}-{max_px_area}"
cache_frames = cache_dir / "frames"
cache_name = cache_dir / "data.json"
if cache_name.exists():
logger.info(f"use_cache: {cache_name}")
return json.load(cache_name.open('r'))
else:
cache_dir.mkdir(parents=True, exist_ok=True)
data = {
'fps': fps,
'frames': []
}
})
return contents
if __name__ == "__main__":
# pass
import argparse
import sys
import tempfile
parser = argparse.ArgumentParser(description="Video2Frame 测试脚本")
parser.add_argument(
"--url",
default=r"D:\pythonProject\learn\3b3e99cf4ca84c3782503d8817242de2.mp4",
help="测试用 m3u8 地址（默认使用公开测试流）",
)
parser.add_argument(
"--cache-dir",
default=r"C:\Users\lzw\AppData\Local\Temp\video2frame_test_mn26tcy_my",
help="缓存目录（默认自动创建临时目录）",
)
parser.add_argument(
"--start",
type=float,
default=0,
help="截取开始时间，单位：秒（默认 0）",
)
parser.add_argument(
"--end",
type=float,
default=10,
help="截取结束时间，单位：秒（默认 10）",
)
parser.add_argument(
"--fps",
type=float,
default=2.0,
help="抽帧帧率（默认 1.0）",
)
args = parser.parse_args()
cache_dir = args.cache_dir or tempfile.mkdtemp(prefix="video2frame_test_")
print(f"[INFO] 缓存目录: {cache_dir}")
v2f = Video2Frame(cache_dir)
# TEST 1: 基本抽帧
print(
f"\n[TEST 1] to_frames: url={args.url}, "
f"start={args.start}s, end={args.end}s, fps={args.fps}"
)
try:
frames = v2f.to_frames(
url=args.url,
start_utc=args.start,
end_utc=args.end,
fps=args.fps,
roi=None,
max_px_area=200_000,
)
print(f" 成功提取 {len(frames)} 帧")
if frames:
print(f" 首帧: {frames[0].shape}")
print(f" 末帧: {frames[-1].shape}")
except Exception as e:
print(f" 失败: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
# TEST 2: frames2content
print("\n[TEST 2] frames2content")
content = frames2content(frames)
print(f" content 列表长度: {len(content)}")
for item in content[:3]:
preview = (
item["text"][:60] + "..."
if item["type"] == "text"
else item["image_url"]["url"][:60] + "..."
if self.save_frames_enable:
cache_frames.mkdir(parents=True, exist_ok=True)
for f in self.iter_mp4(str(download_video_path), start, end, fps):
frame = f['frame']
time = f['src_frame_time']
fps = f['fps']
data['fps'] = fps
if roi is not None:
x1, y1, x2, y2 = roi
frame = frame[y1:y2, x1:x2]
if max_px_area is not None:
frame = resize_frame(frame, max_px_area)
_, buffer = cv2.imencode('.jpg', frame)
b64_str = base64.b64encode(buffer).decode('utf-8')
data['frames'].append({'time': time, 'image': b64_str})
if self.save_frames_enable:
frame_path = cache_frames / f"{time:015d}.jpg"
cv2.imwrite(frame_path, frame)
cache_name.parent.mkdir(parents=True, exist_ok=True)
json.dump(data, cache_name.open('w'), indent=4)
logger.info(f"save_cache: {cache_name}")
return data
def to_llm_contents(self, url, cache=None, fps=None, start=None, end=None, roi=None, max_px_area=None,
prompt_start=None,
prompt_end=None):
data = self.to_frames(url, cache, fps, start, end, roi, max_px_area)
fps = data['fps']
frames = data['frames']
contents = []
if prompt_start is not None:
contents.append({"type": "text", "text": prompt_start})
video_prompt = (
f"以下是从视频中按时间顺序提取的 {len(frames)} 帧画面，fps={fps}，请将它们视为一个连续的视频进行分析。"
)
print(f" - type={item['type']}: {preview}")
if len(content) > 3:
print(f" ... 还有 {len(content) - 3} 个元素")
print("\n[TEST 3] FootballReplayVideoEvent 进球识别（可选）")
try:
from ..util.football_replay_video_event_by_llm import FootballReplayVideoEvent
except ImportError:
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from util.football_replay_video_event_by_llm import FootballReplayVideoEvent
fbrv = FootballReplayVideoEvent(
base_url="http://192.168.1.59:11434",
model="qwen3.6:35b-a3b-q8_0",
temperature=0.7,
)
event_json = fbrv.video_event_for_contents(content, None)
print(f" 识别结果: {event_json}")
# # TEST 3: ROI + max_px_area
# print("\n[TEST 3] to_frames with roi + max_px_area")
# try:
# end_crop = min(args.start + 5, args.end)
# frames_cropped = v2f.to_frames(
# url=args.url,
# start_utc=args.start,
# end_utc=end_crop,
# fps=1.0,
# roi=None,
# max_px_area=200_000,
# )
# print(f" 成功提取 {len(frames_cropped)} 帧（带 ROI 裁剪和面积缩放）")
# except Exception as e:
# print(f" 跳过/失败: {e}")
#
# # TEST 4: SEI UTC 模式说明（仅演示，需真实含 SEI 的流才能运行）
# print("\n[TEST 4] SEI UTC 绝对时间戳模式（示例代码，需替换为含 SEI 的流）")
# print(
# " # 示例：假设视频第一帧 UTC 为 1715600000，提取 10 秒片段\n"
# " # frames = v2f.to_frames(url, start_utc=1715600000, end_utc=1715600010, fps=1)"
# )
#
# print("\n[INFO] 所有测试完成")
contents.append({"type": "text", "text": video_prompt})
for frame in frames:
contents.append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{frame['image']}"
}
})
if prompt_end is not None:
contents.append({"type": "text", "text": prompt_end})
return contents
if __name__ == '__main__':
from aabd.base.patched_logging import init_logging
import os
os.environ["APP_LOG_TYPE"] = "console"
init_logging()
url = "http://video.mam.miguvideo.com/mnt6/fastclip3/wsc/2026/04/29/df760b8d15394cbc9ed0d0a4a9c4b786_1080PS/29133605/vodtmp/3b3e99cf4ca84c3782503d8817242de2.m3u8"
# url = rf"R:\wdx\202604\20260420_download_football_video\finished\69dd5845dd0412067b8d5587-auto-1776074760653\live\videos\00-14-09-052.mp4"
vf = Video2Frame(rf'D:\Code\migu\aigc-embedding-service\src\football_replay_match\core', save_frames_enable=True)
a = vf.to_llm_contents(url, fps=2, start=1777444502543, end=1777444531063, max_px_area=1920 * 1080 // 6)
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage
model = ChatOpenAI(base_url="http://192.168.1.59:11434/v1", model="Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf",
temperature=0.7, api_key='no_key',
extra_body={"chat_template_kwargs": {"enable_thinking": False}})
result = model.invoke([SystemMessage(content="描述一下视频的内容。"), HumanMessage(content=a)])
print(result)
... ...
... ... @@ -12,10 +12,9 @@ except:
pass
try:
from .core.api import replay_match_event
from .core.api import FootballReplayMatch
except:
from core.api import replay_match_event
from core.api import FootballReplayMatch
pass
from threading import Thread
... ... @@ -51,11 +50,12 @@ def get_callback_func(producer, topic):
def kafka_message_iterator_thread(message_iterator, config, callback_func):
frm = FootballReplayMatch(config)
with message_iterator:
for message in message_iterator:
try:
json_message = json.loads(message)
result = replay_match_event(json_message)
result = frm.replay_match_event(json_message)
task_id = json_message.get("id")
callback_func(task_id, json.dumps(result, ensure_ascii=False))
logger.info(f"Processed task_id={task_id}, result={result}")
... ... @@ -66,6 +66,7 @@ def kafka_message_iterator_thread(message_iterator, config, callback_func):
def main():
message_iter = get_message_iterator(settings.input_kafka)
kafka_producer = get_message_producer(settings.output_kafka)
try:
Thread(target=kafka_message_iterator_thread,
... ...
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... ... @@ -32,24 +40,30 @@ embedding = [
{ name = "elasticsearch" },
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{ name = "confluent-kafka" },
{ name = "langchain-openai" },
{ name = "opencv-contrib-python" },
{ name = "pillow" },
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