眨眼频率、眼动分析、心率、视频录制

reproject/test.py

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