#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
  取出再放回场景下商品轨迹特征比对方式与性能分析
  
Created on Tue Apr  1 17:17:47 2025
@author: wqg
"""
import os
import pickle
import random
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from utils.calsimi import calsiml, calsimi_vs_evts

def read_eventdict(evtpaths):
    evtDict = {}
    for filename in os.listdir(evtpaths):
        evtname, ext = os.path.splitext(filename)
        if ext != ".pickle": continue

        evtpath = os.path.join(evtpaths, filename)
        with open(evtpath, 'rb') as f:  
            evtdata = pickle.load(f)
        evtDict[evtname] = evtdata
    
    
    return evtDict


    
def compute_show_pr(Same, Cross):
    TPFN = len(Same)
    TNFP = len(Cross)

    Recall_Pos, Recall_Neg = [], []
    Precision_Pos, Precision_Neg = [], []
    Correct = []
    Thresh = np.linspace(-0.2, 1, 100)
    for th in Thresh:
        TP = np.sum(Same >= th)
        FN = np.sum(Same < th)
        # FN = TPFN - TP
        
        TN = np.sum(Cross < th)
        FP = np.sum(Cross >= th)
        # FP = TNFP - TN

        
        Precision_Pos.append(TP/(TP+FP+1e-6))
        Precision_Neg.append(TN/(TN+FN+1e-6))
        Recall_Pos.append(TP/(TP+FN+1e-6))
        Recall_Neg.append(TN/(TN+FP+1e-6))
        
        # Recall_Pos.append(TP/TPFN)
        # Recall_Neg.append(TN/TNFP)
        
        
        Correct.append((TN+TP)/(TPFN+TNFP))
        
    fig, ax = plt.subplots()  
    
    ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)')
    ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
    ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
    ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)')
    ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
    
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    
    ax.set_xticks(np.arange(0, 1, 0.1))
    ax.set_yticks(np.arange(0, 1, 0.1))
    ax.grid(True, linestyle='--')
    
    ax.set_title('PrecisePos & PreciseNeg')
    ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")     
    ax.legend()
    plt.show() 
    
    # rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png')
    # plt.savefig(rltpath)  # svg, png, pdf
    
    
    fig, axes = plt.subplots(2,1)
    axes[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black')
    axes[0].set_xlim([-0.2, 1])
    axes[0].set_title(f'TP({len(Same)})')

    axes[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black')
    axes[1].set_xlim([-0.2, 1])
    axes[1].set_title(f'TN({len(Cross)})')
    
    # rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png')
    # plt.savefig(rltpath) 
    

    plt.show()



def trail_to_trail(evtpaths, rltpaths):
    # select the method type of how to calculate the feat similarity of trail
    simType = 2
    
    ##1. read all the ShoppingEvent object in the dir 'evtpaths'
    evtDicts = read_eventdict(evtpaths)
    
    ##2. Combine event object with the same barcode
    barcodes, evtpairDict = [], {}
    for k in evtDicts.keys():
        evt = k.split('_')
        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
        if not condt: continue
        
        barcode = evt[-1]
        if barcode not in evtpairDict.keys():
            evtpairDict[barcode] = []
            barcodes.append(barcode)
        
        evtpairDict[barcode].append(evtDicts[k])
    barcodes = set(barcodes)
    
    AA_list, AB_list = [], []
    for barcode in evtpairDict.keys():
        events = evtpairDict[barcode]
        if len(events)>1:
            evta, evtb = random.sample(events, 2)
            AA_list.append((evta, evtb, "same"))
        
        evtc = random.sample(events, 1)[0]
        
        dset = list(barcodes.symmetric_difference(set([barcode]))) 
        bcd = random.sample(dset, 1)[0]
        evtd = random.sample(evtpairDict[bcd], 1)[0]
        AB_list.append((evtc, evtd, "diff"))
        
    mergePairs = AA_list + AB_list
    
    ##3. calculate the similar of two event: evta, evtb
    new_pirs = []
    for evta, evtb, label in mergePairs:
        similar = calsimi_vs_evts(evta, evtb, simType)
        if similar is None: 
            continue
        new_pirs.append((label, round(similar, 3), evta.evtname[:15], evtb.evtname[:15]))

    ##4. compute PR and showing
    Same = np.array([s for label, s, _, _ in new_pirs if label=="same"])
    Cross = np.array([s for label, s, _, _ in new_pirs if label=="diff"])
    compute_show_pr(Same, Cross)


def main():
    evttypes = ["single_event_V10", "single_event_V5", "performence_V10", "performence_V5"]
    # evttypes = ["single_event_V10"]
    
    for evttype in evttypes:
        evtpaths  = "/home/wqg/dataset/pipeline/contrast/{}/evtobjs/".format(evttype)
        rltpaths = "/home/wqg/dataset/pipeline/yrt/{}/yolos_tracking".format(evttype)
    
        trail_to_trail(evtpaths, rltpaths)



if __name__ == '__main__':
    main()