97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
import cv2
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import threading
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import time
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import queue
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import socket
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import json
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import urllib.request
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import struct
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import mediapipe as mp
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import numpy as np
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from analyzer import MonitorSystem
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mp_face_mesh = mp.solutions.face_mesh
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face_mesh = mp_face_mesh.FaceMesh(
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max_num_faces=1,
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refine_landmarks=True,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5
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)
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def apply_soft_roi(frame):
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h, w = frame.shape[:2]
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rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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black_bg = np.zeros((h, w, 3), dtype=np.uint8)
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results = face_mesh.process(rgb_frame)
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if not results.multi_face_landmarks:
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return frame
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landmarks = results.multi_face_landmarks[0].landmark
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xs = [l.x for l in landmarks]
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ys = [l.y for l in landmarks]
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# 计算人脸框
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face_loc = (
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int(min(ys) * h - 0.1 * h), int(max(xs) * w + 0.1 * w),
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int(max(ys) * h + 0.1 * h), int(min(xs) * w - 0.1 * w)
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)
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pad = 30
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face_loc = (max(0, face_loc[0]-pad), min(w, face_loc[1]+pad),
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min(h, face_loc[2]+pad), max(0, face_loc[3]-pad))
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top = face_loc[0]
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right = face_loc[1]
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bottom = face_loc[2]
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left = face_loc[3]
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scale_factor = 10
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small_bg = cv2.resize(frame, (w // scale_factor, h // scale_factor), interpolation=cv2.INTER_LINEAR)
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# 使用 INTER_NEAREST 马赛克效果
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# 使用 INTER_LINEAR 毛玻璃模糊效果
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blurred_frame = cv2.resize(small_bg, (w, h), interpolation=cv2.INTER_LINEAR)
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face_roi = frame[top:bottom, left:right]
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blurred_frame[top:bottom, left:right] = face_roi
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black_bg[top:bottom, left:right] = face_roi
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return blurred_frame
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def test_compression_efficiency():
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# 1. 读取一张测试图 (或者用摄像头抓一帧)
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cap = cv2.VideoCapture(0)
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time.sleep(5) # 等待摄像头稳定
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while True:
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ret, frame = cap.read()
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cap.release()
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if not ret:
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print("无法读取摄像头")
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return
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# 2. 生成模糊处理后的图
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processed_frame = apply_soft_roi(frame) # 使用你的函数
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# 3. 【关键】模拟网络传输/存储：进行 JPG 编码
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# 这里的 80 代表 JPEG 质量，模拟视频编码过程
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encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 80]
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# 编码原图
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_, encoded_original = cv2.imencode('.jpg', frame, encode_param)
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original_size = len(encoded_original)
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# 编码处理后的图
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_, encoded_processed = cv2.imencode('.jpg', processed_frame, encode_param)
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processed_size = len(encoded_processed)
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# 4. 对比结果
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print(f"原始画面编码后大小: {original_size / 1024:.2f} KB")
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print(f"ROI处理编码后大小: {processed_size / 1024:.2f} KB")
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savings = (original_size - processed_size) / original_size * 100
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print(f"📉 带宽/存储节省了: {savings:.2f}%")
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# 可视化对比
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cv2.imshow("Original", frame)
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cv2.imshow("Processed (Bandwidth Saver)", processed_frame)
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# 运行测试
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# 注意：你需要先定义 face_mesh 和 apply_soft_roi 才能运行
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test_compression_efficiency() |