# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 11:17:29 2024

@author: ym
"""

import os
import cv2
import pickle
import numpy as np
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt

def save_imgpairs(barcode, imgpaths, matrix, savepath, thresh=(0.4, 0.6), ctype="intra"):
    if ctype=="intra":
        rows, cols = np.triu_indices(matrix.shape[0], k=1)   # k=1 表示不包括对角线
        mask = matrix[rows, cols] < thresh[1]
        indices = list(zip(rows[mask], cols[mask]))
    else:
        rows, cols = np.where(matrix > thresh[0])
        indices = list(zip(rows, cols))
    
    
    if len(indices):
        savepath = os.path.join(savepath, barcode)
        if not os.path.exists(savepath):
            os.makedirs (savepath)
    
    
    for idx1, idx2 in indices:
        if len(imgpaths) == 1:
            img1 = cv2.imread(imgpaths[0][idx1])
            img2 = cv2.imread(imgpaths[0][idx2])
        elif len(imgpaths) == 2:
            img1 = cv2.imread(imgpaths[0][idx1])
            img2 = cv2.imread(imgpaths[1][idx2])
            
            
        
        simi = matrix[idx1, idx2]

        H1, W1 = img1.shape[:2]
        H2, W2 = img2.shape[:2]
        H, W = max((H1, H2)), max((W1, W2))
        img = np.ones((H, 2*W, 3), dtype=np.uint8) *np.array([255, 128, 128])
        
        img[0:H1, 0:W1, :] = img1
        img[0:H2, (2*W-W2):, :] = img2
        
        text = f"sim: {simi:.2f}"
        org = (10, H-10)
        cv2.putText(img, text, org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.75, 
                    color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA)
        imgpath = os.path.join(savepath, f"{simi:.2f}_{barcode}_{idx1}_{idx2}.png")
        cv2.imwrite(imgpath, img)
        


def feat_analysis(featpath):   
    '''
    标准特征集中样本类内、类间相似度分布
    '''
    
    
    
    savepath = r"D:\exhibition\result\stdfeat"

    InterThresh = (0.4, 0.6)
    
    featDict, features= [], []
    for filename in os.listdir(featpath):
        file, ext = os.path.splitext(filename)
        if ext != ".pickle": continue
        
        filepath = os.path.join(featpath, filename)
        with open(filepath, 'rb') as f:  
            bpDict = pickle.load(f)

            feat = bpDict["feats_ft32"]

            featDict.append(bpDict)
            features.append(feat)
    N = len(features)   
   
    simMatrix = []
    intra_simi = np.empty(0)   
    low_simi_index = {}
    for i, feats in enumerate(features):
        matrix = 1 - cdist(feats, feats, 'cosine')
        
        simMatrix.append(matrix)
        
        '''提取相似矩阵上三角元素'''
        rows, cols = np.triu_indices(matrix.shape[0], k=1)  # k=1 表示不包括对角线
        upper_tri= matrix[rows, cols]        
        intra_simi = np.concatenate((intra_simi, upper_tri))
        
        '''保存相似度小于阈值的图像对'''
        barcode = featDict[i]["barcode"]
        imgpaths = featDict[i]["imgpaths"]
        # save_imgpairs(barcode, [imgpaths], matrix, savepath, InterThresh, "intra")
        print(f"{barcode} have done!")
        
    Matrix = np.zeros((N, N))
    inter_bcds = []
    inter_simi = np.empty(0)
    for i, feati in enumerate(features):
        bcdi = featDict[i]["barcode"]
        imgpathi = featDict[i]["imgpaths"]
        for j, featj in enumerate(features):
            bcdj = featDict[j]["barcode"]
            imgpathj = featDict[j]["imgpaths"]
        
            matrix = 1 - cdist(feati, featj, 'cosine')
            
            inter_bcds.append((i, j, bcdi, bcdj))
            Matrix[i, j] = np.mean(matrix)
            if j>i:
                bcd_ij = bcdi+'_'+bcdj
                # save_imgpairs(bcd_ij, [imgpathi, imgpathj], matrix, savepath, InterThresh, "inter")
                inter_simi = np.concatenate((inter_simi, matrix.ravel()))
                
                print(f"{bcd_ij} have done!")
            
    fig, axs = plt.subplots(2, 1)
    axs[0].hist(intra_simi, bins=100, color='blue', edgecolor='black', alpha=0.7)
    axs[0].set_xlim(0, 1)
    axs[0].set_xlabel('Performance')
    axs[0].set_title("inter similarity")
    
    axs[1].hist(inter_simi, bins=100, color='green', edgecolor='black', alpha=0.7)
    axs[1].set_xlim(0, 1)
    axs[1].set_xlabel('Performance')
    axs[1].set_title("inter similarity")
    
    
    
    
    
    print("Done！")
            
            
            
    




def main():
    stdpath = r"D:\exhibition\dataset\feats"
    
    feat_analysis(stdpath)
    

if __name__ == '__main__':
    main()