摘要：要实现Python中的打架行为识别，可以使用深度学习技术结合视频分析。以下是分步指南及代码示例：

要实现Python中的打架行为识别，可以使用深度学习技术结合视频分析。以下是分步指南及代码示例：

一、技术路线

二、代码实现（使用PyTorch + OpenCV）

python

import cv2

import torch

import numpy as np

from torchvision import transforms

from models import i3d # 需要自定义或使用预训练模型

# 1. 加载预训练模型

model = i3d.InceptionI3d(400, in_channels=3)

model.load_state_dict(torch.load('i3d_pretrained.pth'))

model.eval

# 2. 视频预处理

def preprocess_frame(frame):

transform = transforms.Compose([

transforms.ToPILImage,

transforms.Resize((224, 224)),

transforms.ToTensor,

transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

])

return transform(frame)

# 3. 滑动窗口检测

def detect_fight(video_path, window_size=16):

cap = cv2.VideoCapture(video_path)

frames =

predictions =

while cap.isOpened:

ret, frame = cap.read

if not ret:

break

# 预处理并收集帧

processed = preprocess_frame(frame)

frames.append(processed)

# 达到窗口大小时进行预测

if len(frames) == window_size:

clip = torch.stack(frames).permute(1, 0, 2, 3)

with torch.no_grad:

outputs = model(clip)

prob = torch.nn.functional.softmax(outputs, dim=1)

predictions.append(prob[0][1].item) # 假设索引1是打架类别

frames = # 重置窗口

cap.release

return predictions

# 4. 后处理（示例：简单阈值法）

def post_process(predictions, threshold=0.7):

return [1 if p > threshold else 0 for p in predictions]

# 使用示例

predictions = detect_fight("test_video.mp4")

results = post_process(predictions)

print("检测到打架的窗口:", [i for i, x in enumerate(results) if x == 1])

三、关键改进点

python

# 结合rgb和光流特征

rgb_stream = model_rgb(clip_rgb)

flow_stream = model_flow(clip_flow)

combined = 0.6 * rgb_stream + 0.4 * flow_stream

python

import openpose # 需要OpenPose库

op = openpose.OpenPose

def analyze_pose(frame):

poses = op.detect(frame)

violence_score = 0

for pose in poses:

# 计算肢体速度/角度异常

speed = calculate_limb_speed(pose)

if speed > THRESHOLD:

violence_score += 1

return violence_score

四、训练建议

python

train_transform = transforms.Compose([

RandomTemporalShift(max_shift=8),

RandomSpatialCrop,

ColorJitterHLS

])

python

class FocalLoss(nn.Module):

def __init__(self, alpha=0.25, gamma=2):

super.__init__

self.alpha = alpha

self.gamma = gamma

def forward(self, inputs, targets):

BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')

pt = torch.exp(-BCE_loss)

loss = self.alpha * (1-pt)**self.gamma * BCE_loss

return loss.mean

五、部署优化

python

# 使用知识蒸馏

teacher_model = load_pretrained_i3d

student_model = MobileNet3D

distill_loss = KLDivLoss

bash

trtexec --onnx=model.onnx --saveEngine=model.engine --fp16

六、评估指标

推荐使用：

完整实现需要：

建议从预训练模型开始微调，逐步加入领域特定数据。实际部署时需考虑光照变化、遮挡等现实因素。

来源：老客数据一点号