367 lines
14 KiB
Python
367 lines
14 KiB
Python
import os
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import json
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from datetime import datetime, timedelta
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from kafka import KafkaConsumer
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from decord import VideoReader, cpu
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from PIL import Image
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import redis
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from redis import Redis
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import io
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import re
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import threading
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import requests
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import base64
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import traceback
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import json
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from config import *
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# 配置
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OLLAMA_URL = OLLAMA_URL
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KAFKA_BROKER = KAFKA_BROKER
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KAFKA_TOPIC = WORKER_CONFIGS["cpm"]["kafka_topic"]
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KAFKA_GROUP_ID = f"cpm_{KAFKA_GROUP_ID_PREFIX}"
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REDIS_HOST = REDIS_HOST
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REDIS_PORT = REDIS_PORT
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REDIS_PASSWORD = REDIS_PASSWORD
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REDIS_DB = WORKER_CONFIGS["cpm"]["redis_db"] # Worker使用的Redis DB
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MAIN_REDIS_DB = MAIN_REDIS_DB # 主Redis DB
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UPLOAD_DIR = UPLOAD_DIR
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RESULT_DIR = RESULT_DIR
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# 确保目录存在
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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os.makedirs(RESULT_DIR, exist_ok=True)
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# 初始化 Kafka
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consumer = KafkaConsumer(
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KAFKA_TOPIC,
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bootstrap_servers=[KAFKA_BROKER],
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group_id=KAFKA_GROUP_ID,
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auto_offset_reset='earliest',
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enable_auto_commit=True,
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value_deserializer=lambda x: json.loads(x.decode('utf-8'))
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)
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# 初始化 Redis
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redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=REDIS_DB
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)
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main_redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=MAIN_REDIS_DB
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)
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# 设置 GPU 设备
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# torch.cuda.set_device(0)
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class MediaAnalysisSystem:
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def __init__(self):
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self.MAX_NUM_FRAMES = 16
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def encode_video(self, video_data):
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def uniform_sample(l, n):
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gap = len(l) / n
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return [l[int(i * gap + gap / 2)] for i in range(n)]
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video_file = io.BytesIO(video_data)
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vr = VideoReader(video_file, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1)
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frame_idx = list(range(0, len(vr), sample_fps))
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if len(frame_idx) > self.MAX_NUM_FRAMES:
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frame_idx = uniform_sample(frame_idx, self.MAX_NUM_FRAMES)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype('uint8')) for v in frames]
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print('num frames:', len(frames))
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return frames
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def process_video(self, video_data, object_name):
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if not video_data:
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raise ValueError(f"Empty video data for {object_name}")
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print(f"Processing video: {object_name}, data size: {len(video_data)} bytes")
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frames = self.encode_video(video_data)
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question = """请对这段监控视频进行详细分析，包括以下方面：
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1. 场景中人数的精确统计
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2. 每个人的个人行为分析
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3. 面部表情识别和情绪状态评估
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4. 整体场景和环境的详细描述
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5. 人与人之间的互动情况
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6. 时间和环境条件（如果可见）
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7. 任何可疑或异常活动
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8. 人员的具体特征（估计年龄范围、性别、着装）
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9. 人员的移动模式和方向
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10. 携带的物品或物体
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11. 群体动态和聚集情况
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12. 视频中的时间戳分析（如果有）
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请用清晰、有条理的格式描述，并突出重要发现。"""
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encoded_frames = [self.image_to_base64(frame) for frame in frames]
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payload = {
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"model": "minicpm-v",
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"prompt": question,
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"images": encoded_frames
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}
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try:
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response = requests.post(OLLAMA_URL, json=payload, stream=True)
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print(f"Ollama API 响应状态码: {response.status_code}")
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print(f"Ollama API 响应头: {response.headers}")
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if response.status_code == 200:
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answer = self.process_stream_response(response)
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else:
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raise Exception(f"Ollama API 错误: {response.status_code}")
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except requests.RequestException as e:
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print(f"请求 Ollama API 时出错: {str(e)}")
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raise
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extracted_info = self.extract_info(answer)
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return {
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"original_answer": answer,
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"extracted_info": extracted_info,
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"num_frames": len(frames),
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}
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def process_image(self, image_data, object_name):
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image = Image.open(io.BytesIO(image_data))
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question = """请对这张监控图像进行详细分析，包括以下方面：
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1. 场景中人数的精确统计
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2. 每个人的个人行为分析
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3. 面部表情识别和情绪状态评估
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4. 整体场景和环境的详细描述
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5. 人与人之间的互动情况
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6. 时间和环境条件（如果可见）
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7. 任何可疑或异常活动
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8. 人员的具体特征（估计年龄范围、性别、着装）
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9. 人员的位置和姿态
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10. 携带的物品或物体
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11. 群体动态和聚集情况
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12. 图像中的时间戳信息（如果有）
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请用清晰、有条理的格式描述，并突出重要发现。"""
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encoded_image = self.image_to_base64(image)
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payload = {
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"model": "minicpm-v",
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"prompt": question,
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"images": [encoded_image]
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}
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try:
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response = requests.post(OLLAMA_URL, json=payload, stream=True)
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if response.status_code == 200:
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answer = self.process_stream_response(response)
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else:
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raise Exception(f"Ollama API 错误: {response.status_code}")
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except requests.RequestException as e:
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print(f"请求 Ollama API 时出错: {str(e)}")
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raise
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extracted_info = self.extract_info(answer)
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return {
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"original_answer": answer,
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"extracted_info": extracted_info,
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"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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def process_stream_response(self, response):
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full_response = []
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for line in response.iter_lines():
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if line:
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try:
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json_response = json.loads(line)
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if 'response' in json_response:
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full_response.append(json_response['response'])
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if json_response.get('done', False):
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break
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except json.JSONDecodeError:
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print(f"无法解析 JSON 行: {line}")
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return ''.join(full_response)
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@staticmethod
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def image_to_base64(image):
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode()
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@staticmethod
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def extract_time_from_filename(object_name):
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filename = os.path.basename(object_name)
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time_str = filename.split('_')[0] + '_' + filename.split('_')[1].split('.')[0]
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try:
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start_time = datetime.strptime(time_str, "%Y%m%d_%H%M%S")
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end_time = start_time + timedelta(seconds=10)
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return start_time, end_time
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except ValueError:
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print(f"无法从文件名 '{filename}' 解析时间。使用默认时间。")
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return datetime.now(), datetime.now() + timedelta(seconds=10)
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@staticmethod
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def extract_info(answer):
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info = {
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"environment": None,
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"num_people": None,
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"actions": [],
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"interactions": [],
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"objects": [],
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"furniture": []
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}
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environments = ["办公室", "室内", "��外", "会议室"]
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for env in environments:
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if env in answer.lower():
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info["environment"] = env
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break
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people_patterns = [
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r'(\d+)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一|二|三|四|五|六|七|八|九|十)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一个|几个)\s*(人|个人|员工|用户|小朋友|成年人|女性|男性)',
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r'几\s*(名|位)\s*(人|员工|用户|小朋友|成年人|女性|男性)?',
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r'(男|女)(性|生|士)',
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r'(成年|未成年|青少年|老年)\s*(人|群体)',
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r'(员工|职工|工人|学生|顾客|观众|游客|乘客)',
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r'(群众|民众|大众|公众)',
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r'(男女|老少|老幼|大人|小孩)'
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]
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for pattern in people_patterns:
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match = re.search(pattern, answer)
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if match:
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if match.group(1).isdigit():
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info["num_people"] = int(match.group(1))
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elif match.group(1) in ['一个', '一']:
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info["num_people"] = 1
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else:
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num_word_to_digit = {
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'二': 2, '三': 3, '四': 4, '五': 5,
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'六': 6, '七': 7, '八': 8, '九': 9, '十': 10
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}
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info["num_people"] = num_word_to_digit.get(match.group(1), 0)
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break
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actions = ["坐", "站", "摔倒", "跳舞", "转身", "摔", "倒", "倒下", "躺下", "转身", "跳跃", "跳", "躺", "睡", "说话"]
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for action in actions:
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if action in answer:
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info["actions"].append(action)
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interactions = ["互动", "交流", "身体语言", "交谈", "讨论", "开会"]
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for interaction in interactions:
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if interaction in answer:
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info["interactions"].append(interaction)
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objects = ["水瓶", "办公用品", "文件", "电脑"]
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furniture = ["椅子", "桌子", "咖啡桌", "文件柜", "床", "沙发"]
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for obj in objects:
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if obj in answer:
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info["objects"].append(obj)
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for item in furniture:
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if item in answer:
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info["furniture"].append(item)
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return info
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# 初始化 MediaAnalysisSystem
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media_analysis_system = MediaAnalysisSystem()
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def process_task():
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for message in consumer:
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print(f"收到Kafka消息: topic={message.topic}, partition={message.partition}, offset={message.offset}")
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task = message.value
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task_id = task['task_id']
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filename = task['filename']
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file_type = task['file_type']
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print(f"解析任务信息: ID={task_id}, filename={filename}, type={file_type}")
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file_path = os.path.join(UPLOAD_DIR, filename)
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task_key = f"task:{task_id}"
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try:
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key_type = main_redis_client.type(task_key)
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if key_type != b'hash':
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main_redis_client.delete(task_key)
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main_redis_client.hset(task_key, "status", "processing")
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print(f"任务 {task_id} 状态更新为 'processing'")
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if file_type == "image":
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print(f"Processing image: {filename}")
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with open(file_path, 'rb') as f:
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image_data = f.read()
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result = media_analysis_system.process_image(image_data, filename)
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else: # video
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print(f"Processing video: {filename}")
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with open(file_path, 'rb') as f:
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video_data = f.read()
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result = media_analysis_system.process_video(video_data, filename)
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if result:
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redis_client.hset(f"cpm_result:{task_id}", mapping={
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"result": json.dumps(result),
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"result_file": filename
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})
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main_redis_client.hset(task_key, mapping={
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"status": "completed",
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"result_type": "cpm",
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"result_key": f"cpm_result:{task_id}"
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})
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print(f"{file_type.capitalize()} {filename} 已处理，结果已保存")
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else:
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print(f"{file_type.capitalize()} {filename} 处理失败")
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main_redis_client.hset(task_key, "status", "failed")
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except Exception as e:
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print(f"处理任务 {task_id} 时出错: {str(e)}")
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print(f"错误详情: {traceback.format_exc()}")
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main_redis_client.hset(task_key, mapping={
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"status": "failed",
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"error": str(e)
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})
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print(f"任务 {task_id} 处理完毕,等待下一个Kafka消息...")
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def listen_redis_changes():
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pubsub = redis_client.pubsub()
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pubsub.psubscribe('__keyspace@3__:cpm_result:*') # Listen for changes on all cpm_result keys
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for message in pubsub.listen():
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if message['type'] == 'pmessage':
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key = message['channel'].decode('utf-8').split(':')[-1]
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operation = message['data'].decode('utf-8')
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if operation == 'hset':
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value = redis_client.hgetall(f"cpm_result:{key}")
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if value:
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result = {k.decode(): v.decode() for k, v in value.items()}
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print(f"Result update for task {key}: {result}")
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if __name__ == "__main__":
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print("cpm处理程序启动...")
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# Start the task processing thread
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task_thread = threading.Thread(target=process_task, daemon=True)
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task_thread.start()
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print("任务处理线程已启动")
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# Start the Redis listening thread
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redis_thread = threading.Thread(target=listen_redis_changes, daemon=True)
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redis_thread.start()
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print("Redis监听线程已启动")
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print("主程序进入等待状态...")
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# Keep the main thread running
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task_thread.join()
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redis_thread.join() |