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()