bakeup

contrast/feat_similar.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  9 10:36:45 2024
+
+@author: ym
+"""
+import os
+import cv2
+import numpy as np
+import torch
+import sys
+from scipy.spatial.distance import cdist
+sys.path.append(r"D:\DetectTracking")
+from tracking.trackers.reid.reid_interface import ReIDInterface 
+from tracking.trackers.reid.config import config as ReIDConfig
+ReIDEncoder = ReIDInterface(ReIDConfig)
+
+
+def inference_image(images):
+    batch_patches = []
+    patches = []
+    for d, img1 in enumerate(images):
+
+
+        img = img1[:, :, ::-1].copy()    # the model expects RGB inputs
+        patch = ReIDEncoder.transform(img)
+        
+        # patch = patch.to(device=self.device).half()
+        if str(ReIDEncoder.device) != "cpu":
+            patch = patch.to(device=ReIDEncoder.device).half()
+        else:
+            patch = patch.to(device=ReIDEncoder.device)
+        
+        patches.append(patch)
+        if (d + 1) % ReIDEncoder.batch_size == 0:
+            patches = torch.stack(patches, dim=0)
+            batch_patches.append(patches)
+            patches = []
+
+    if len(patches):
+        patches = torch.stack(patches, dim=0)
+        batch_patches.append(patches)
+
+    features = np.zeros((0, ReIDEncoder.embedding_size))
+    for patches in batch_patches:
+        pred = ReIDEncoder.model(patches)
+        pred[torch.isinf(pred)] = 1.0
+        feat = pred.cpu().data.numpy()
+        features = np.vstack((features, feat))
+
+    return features
+
+
+def similarity_compare(root_dir):
+    '''
+    root_dir：包含 "subimgs"字段的文件夹中图像为 subimg子图
+    功能：相邻帧子图间相似度比较
+    '''
+    
+    all_files = []
+    extensions = ['.png', '.jpg']
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        filepaths = []
+        for filename in filenames:
+            if os.path.basename(dirpath).find('subimgs') < 0: 
+                continue
+            file, ext = os.path.splitext(filename)
+            if ext in extensions:
+                imgpath = os.path.join(dirpath, filename)
+                filepaths.append(imgpath)
+        nf = len(filepaths)
+        if nf==0:
+            continue
+
+        fnma = os.path.basename(filepaths[0]).split('.')[0]
+        imga = cv2.imread(filepaths[0])
+        ha, wa = imga.shape[:2]
+        
+        for i in range(1, nf):
+            fnmb = os.path.basename(filepaths[i]).split('.')[0]
+            
+            imgb = cv2.imread(filepaths[i])
+            hb, wb = imgb.shape[:2]
+            
+            
+            feats = inference_image(((imga, imgb)))
+            
+            similar = 1 - np.maximum(0.0, cdist(feats, feats, metric='cosine')) 
+            
+            
+            h, w  = max((ha, hb)), max((wa, wb))
+            img = np.zeros(((h, 2*w, 3)), np.uint8)
+            img[0:ha, 0:wa], img[0:hb, w:(w+wb)] = imga, imgb
+
+            linewidth = max(round(((h+2*w))/2 * 0.001), 2)
+            cv2.putText(img,
+                        text=f'{similar[0,1]:.2f}',     # Text string to be drawn
+                        org=(max(w-20, 10), h-10),       # Bottom-left corner of the text string
+                        fontFace=0,                     # Font type
+                        fontScale=linewidth/3,          # Font scale factor
+                        color=(0, 0, 255),              # Text color
+                        thickness=linewidth,            # Thickness of the lines used to draw a text
+                        lineType=cv2.LINE_AA,           # Line type
+                        )                           
+            spath = os.path.join(dirpath, 's'+fnma+'-vs-'+fnmb+'.png')
+            cv2.imwrite(spath, img)
+            
+            
+            fnma = os.path.basename(filepaths[i]).split('.')[0]
+            imga = imgb.copy()
+            ha, wa = imga.shape[:2]
+                
+
+                
+    return
+
+
+def main():
+    root_dir = r"D:\contrast\dataset\result\20240723-112242_6923790709882"
+    
+    try:
+        similarity_compare(root_dir)   
+    except Exception as e:
+        print(f'Error: {e}') 
+    
+
+
+
+if __name__ == '__main__':
+    main()
+