更新 detacttracking

detecttracking/contrast/stdfeat_analys.py

@@ -0,0 +1,160 @@
+# -*- 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):    
+    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()
+    
+    
+    
+    
+    
+    
+    
+    
+