# -*- coding: utf-8 -*-
"""
Created on Wed Sep 11 11:57:30 2024
    永辉现场试验输出数据的 1:1 性能评估
    适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
@author: ym
"""
import os
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import sys

sys.path.append(r"D:\DetectTracking")
from tracking.utils.read_data import read_similar

def read_one2one_data(filepath):
    simiList = []
    with open(filepath, 'r', encoding='utf-8') as f:
        lines = f.readlines()
        
        split_flag = False
        simi_dict = {}
        for i, line in enumerate(lines):
            line = line.strip()
            if not line:
                if len(simi_dict): simiList.append(simi_dict)
                simi_dict = {}
                continue

            label = line.split(':')[0].strip()
            value = line.split(':')[1].strip()

            if label.find("SeqDir") >= 0:
                simi_dict["SeqDir"] = value
            if label.isdigit() and len(label) >= 8:
                
                simi_max, simi_min = value.strip(',').split('.')
                simi_dict["barcode"] = label
                simi_dict["simi_max"] = float(simi_max) / 1000
                simi_dict["simi_min"] = float(simi_min) / 1000
        
        if len(simi_dict): simiList.append(simi_dict)
                
    return simiList

def plot_pr_curve(matrix):
    
    simimax, simimean = [], []
    need_analysis = []
    for simidict in matrix:
        simimax.append(simidict["simi_max"])
        simimean.append(simidict["simi_min"])
        if simidict["simi_max"]>0.6:
            need_analysis.append(simidict)

    simimax = np.array(simimax)
    simimean = np.array(simimean)

    TPFN_max = len(simimax)
    TPFN_mean = len(simimean)

    fig, axs = plt.subplots(2, 1)
    axs[0].hist(simimax, bins=60, edgecolor='black')
    axs[0].set_xlim([-0.2, 1])
    axs[0].set_title(f'Same Barcode, Num: {TPFN_max}')
    axs[1].hist(simimean, bins=60, edgecolor='black')
    axs[1].set_xlim([-0.2, 1])
    axs[1].set_title(f'Cross Barcode, Num: {TPFN_mean}')
    # plt.savefig(f'./result/{file}_hist.png')  # svg, png, pdf
 
    Recall_Neg = []
    Thresh = np.linspace(-0.2, 1, 100)
    for th in Thresh:
        TN = np.sum(simimax < th)
        Recall_Neg.append(TN/TPFN_max)

    fig, ax = plt.subplots()  
    ax.plot(Thresh, Recall_Neg, 'b', label='Recall_Pos: TP/TPFN')
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    ax.grid(True)
    ax.set_title('Positive recall')
    ax.set_xlabel(f"Num: {TPFN_max}")     
    ax.legend()
    plt.show() 
    # plt.savefig(f'./result/{file}_pr.png')  # svg, png, pdf
    
    print("Have done!") 
    pass

                
def test_compare():
    filepaths = [r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_1\OneToOneCompare.txt",
                 r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_2\OneToOneCompare.txt",
                 r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_1\OneToOneCompare.txt",
                 r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_2\OneToOneCompare.txt"
                ]
    
    simiList = []
    for fp in filepaths:
        slist = read_one2one_data(fp)
        simiList.extend(slist)

    plot_pr_curve(simiList)
    
def one2one_pr(paths): 
    paths = Path(paths)
    evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2]
    
    events, similars = [], []
    
    ##===================================== 扫A放A, 扫A放B场景
    one2oneAA, one2oneAB = [], []
    
    ##===================================== 应用于展厅 1：N
    tp_events, fn_events, fp_events, tn_events = [], [], [], []
    tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], []
    
    ##===================================== 应用于1:n
    tpevents, fnevents, fpevents, tnevents = [], [], [], []
    tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []

    for path in evtpaths:
        barcode = path.stem.split('_')[-1]
        datapath = path.joinpath('process.data')
        
        if not barcode.isdigit() or len(barcode)<10: continue
        if not datapath.is_file(): continue
    
        try: 
            SimiDict = read_similar(datapath)
        except Exception as e:
            print(f"{path.stem}, Error: {e}")
        
        one2one = SimiDict['one2one']
        one2n = SimiDict['one2n']
        
        barcodes, similars = [], []
        for dt in one2one:
            barcodes.append(dt['barcode'])
            similars.append(dt['similar'])
        
        if len(barcodes)!=len(similars) or len(barcodes)==0:
            continue
            
        ##===================================== 扫A放A, 扫A放B场景    
        simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] 
        simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
        
        one2oneAA.extend(simAA)
        one2oneAB.extend(simAB)
        
        
        ##===================================== 以下应用适用于展厅 1：N
        max_idx = similars.index(max(similars))
        max_sim = similars[max_idx]
        # max_bcd = barcodes[max_idx]
        
        for i in range(len(one2one)):
            bcd, simi = barcodes[i], similars[i]
            if bcd==barcode and simi==max_sim:
                tp_simi.append(simi)
                tp_events.append(path.stem)
            elif bcd==barcode and simi!=max_sim:
                fn_simi.append(simi)
                fn_events.append(path.stem)
            elif bcd!=barcode and simi!=max_sim: 
                tn_simi.append(simi)
                tn_events.append(path.stem)
            else:
                fp_simi.append(simi)
                fp_events.append(path.stem)
        
                
        ##===================================== 以下应用适用1:n 
        events, evt_barcodes, evt_similars, evt_types = [], [], [], []        
        for dt in one2n:
            events.append(dt["event"])
            evt_barcodes.append(dt["barcode"])
            evt_similars.append(dt["similar"])
            evt_types.append(dt["type"])
        
        if len(events)!=len(evt_barcodes) or len(evt_barcodes)!=len(evt_similars) \
            or len(evt_barcodes)!=len(evt_similars) or len(events)==0: continue 
  
        maxsim = evt_similars[evt_similars.index(max(evt_similars))]
        for i in range(len(one2n)):
            bcd, simi = evt_barcodes[i], evt_similars[i]
            
            if bcd==barcode and simi==maxsim:
                tpsimi.append(simi)
                tpevents.append(path.stem)
            elif bcd==barcode and simi!=maxsim:
                fnsimi.append(simi)
                fnevents.append(path.stem)
            elif bcd!=barcode and simi!=maxsim: 
                tnsimi.append(simi)
                tnevents.append(path.stem)
            else:
                fpsimi.append(simi)
                fpevents.append(path.stem)
            
    '''命名规则:
          1:1              1:n                1:N
          TP_              TP                 TPX
        PPrecise_       PPrecise           PPreciseX
                         tpsimi             tp_simi  
    '''
    
    ''' 1:1 数据存储'''
    PPrecise_, PRecall_ = [], []
    NPrecise_, NRecall_ = [], []
    
    ''' 1:n 数据存储'''
    PPrecise, PRecall = [], []
    NPrecise, NRecall = [], []
    
    ''' 展厅 1:N 数据存储'''
    PPreciseX, PRecallX = [], []
    NPreciseX, NRecallX = [], []
    
    Thresh = np.linspace(-0.2, 1, 100)
    for th in Thresh:
        '''============================= 1:1'''
        TP_ = sum(np.array(one2oneAA) >= th)
        FP_ = sum(np.array(one2oneAB) >= th)
        FN_ = sum(np.array(one2oneAA) < th)
        TN_ = sum(np.array(one2oneAB) < th)
        PPrecise_.append(TP_/(TP_+FP_+1e-6))
        PRecall_.append(TP_/(TP_+FN_+1e-6))
        NPrecise_.append(TN_/(TN_+FN_+1e-6))
        NRecall_.append(TN_/(TN_+FP_+1e-6))
        
        '''============================= 1:n'''
        TP = sum(np.array(tpsimi) >= th)
        FP = sum(np.array(fpsimi) >= th)
        FN = sum(np.array(fnsimi) < th)
        TN = sum(np.array(tnsimi) < th)
        PPrecise.append(TP/(TP+FP+1e-6))
        PRecall.append(TP/(TP+FN+1e-6))
        NPrecise.append(TN/(TN+FN+1e-6))
        NRecall.append(TN/(TN+FP+1e-6))
        
        
        '''============================= 1:N 展厅'''
        TPX = sum(np.array(tp_simi) >= th)
        FPX = sum(np.array(fp_simi) >= th)
        FNX = sum(np.array(fn_simi) < th)
        TNX = sum(np.array(tn_simi) < th)
        PPreciseX.append(TPX/(TPX+FPX+1e-6))
        PRecallX.append(TPX/(TPX+FNX+1e-6))
        NPreciseX.append(TNX/(TNX+FNX+1e-6))
        NRecallX.append(TNX/(TNX+FPX+1e-6))
    
    '''============================= 1:1 曲线'''
    fig, ax = plt.subplots()
    ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
    ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
    ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
    ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    ax.grid(True)
    ax.set_title('Precise & Recall')
    ax.set_xlabel(f"Num: {len(evtpaths)}")     
    ax.legend()
    plt.show()
    
    '''============================= 1:1 直方图'''
    fig, axes = plt.subplots(2, 1)
    axes[0].hist(np.array(one2oneAA), bins=60, edgecolor='black')
    axes[0].set_xlim([-0.2, 1])
    axes[0].set_title('AA')
    axes[1].hist(np.array(one2oneAB), bins=60, edgecolor='black')
    axes[1].set_xlim([-0.2, 1])
    axes[1].set_title('BB')
    plt.show()
        
    '''============================= 1:n 曲线''' 
    fig, ax = plt.subplots()
    ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
    ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
    ax.plot(Thresh, NPrecise, 'g', label='Precise_Neg: TN/TNFP')
    ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    ax.grid(True)
    ax.set_title('Precise & Recall')
    ax.set_xlabel(f"Num: {len(evtpaths)}")     
    ax.legend()
    plt.show()
    
    '''============================= 1:n 直方图'''
    fig, axes = plt.subplots(2, 2)
    axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
    axes[0, 0].set_xlim([-0.2, 1])
    axes[0, 0].set_title('TP')
    axes[0, 1].hist(fpsimi, bins=60, edgecolor='black')
    axes[0, 1].set_xlim([-0.2, 1])
    axes[0, 1].set_title('FP')
    axes[1, 0].hist(tnsimi, bins=60, edgecolor='black')
    axes[1, 0].set_xlim([-0.2, 1])
    axes[1, 0].set_title('TN')
    axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
    axes[1, 1].set_xlim([-0.2, 1])
    axes[1, 1].set_title('FN')
    plt.show()
    
    
    '''============================= 1:N 展厅 曲线''' 
    fig, ax = plt.subplots()
    ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
    ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
    ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
    ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    ax.grid(True)
    ax.set_title('Precise & Recall')
    ax.set_xlabel(f"Num: {len(evtpaths)}")     
    ax.legend()
    plt.show()
    
    '''============================= 1:N 展厅 直方图'''
    fig, axes = plt.subplots(2, 2)
    axes[0, 0].hist(tp_simi, bins=60, edgecolor='black')
    axes[0, 0].set_xlim([-0.2, 1])
    axes[0, 0].set_title('TP')
    axes[0, 1].hist(fp_simi, bins=60, edgecolor='black')
    axes[0, 1].set_xlim([-0.2, 1])
    axes[0, 1].set_title('FP')
    axes[1, 0].hist(tn_simi, bins=60, edgecolor='black')
    axes[1, 0].set_xlim([-0.2, 1])
    axes[1, 0].set_title('TN')
    axes[1, 1].hist(fn_simi, bins=60, edgecolor='black')
    axes[1, 1].set_xlim([-0.2, 1])
    axes[1, 1].set_title('FN')
    plt.show()
    
    

    
    print('Done!')
            

    

if __name__ == "__main__":
    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1120_展厅模型v801测试\扫A放A"
    one2one_pr(evtpaths)