modify dotrack module

featureVal.py

@@ -0,0 +1,218 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri May 31 14:50:21 2024
+
+@author: ym
+"""
+import cv2
+import numpy as np
+import torch
+from scipy.spatial.distance import cdist
+from tracking.trackers.reid.config import config as ReIDConfig
+from tracking.trackers.reid.reid_interface import ReIDInterface 
+ReIDEncoder = ReIDInterface(ReIDConfig)
+
+def read_data_file(datapath):
+
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    Videos = []
+    FrameBoxes, FrameFeats = [], []
+    boxes, feats = [], []
+    
+    bboxes, ffeats = [], []
+    timestamp = []
+    t1 = None
+    for line in lines: 
+        if line.find('CameraId') >= 0: 
+            t = int(line.split(',')[1].split(':')[1])
+            timestamp.append(t)
+     
+            if len(boxes) and len(feats):
+                FrameBoxes.append(np.array(boxes, dtype = np.float32))
+                FrameFeats.append(np.array(feats, dtype = np.float32))
+                
+                boxes, feats = [], []
+            
+            if t1 and t - t1 > 1e4:
+                Videos.append((FrameBoxes, FrameFeats))    
+                FrameBoxes, FrameFeats = [], []
+            t1 = int(line.split(',')[1].split(':')[1])
+            
+        if line.find('box') >= 0:
+            box = line.split(':', )[1].split(',')[:-1]
+            boxes.append(box)
+            bboxes.append(boxes)
+            
+        
+        if line.find('feat') >= 0:
+            feat = line.split(':', )[1].split(',')[:-1]
+            feats.append(feat)
+            ffeats.append(feat)
+    
+    
+    
+    
+    FrameBoxes.append(np.array(boxes, dtype = np.float32))
+    FrameFeats.append(np.array(feats, dtype = np.float32))  
+    Videos.append((FrameBoxes, FrameFeats)) 
+    
+    TimeStamp = np.array(timestamp, dtype = np.float32) 
+    DimesDiff = np.diff((timestamp))      
+
+    return Videos
+
+def inference_image(image, detections):
+    H, W, _ = np.shape(image)
+    imgs = []
+    batch_patches = []
+    patches = []
+    for d in range(np.size(detections, 0)):
+        tlbr = detections[d, :4].astype(np.int_)
+        tlbr[0] = max(0, tlbr[0])
+        tlbr[1] = max(0, tlbr[1])
+        tlbr[2] = min(W - 1, tlbr[2])
+        tlbr[3] = min(H - 1, tlbr[3])
+        img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
+
+        img = img1[:, :, ::-1].copy()    # the model expects RGB inputs
+        patch = ReIDEncoder.transform(img)
+        
+        imgs.append(img1)
+        # 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 imgs, features
+
+def test_dog():
+    
+    
+    datapath = r"D:\datasets\ym\Img_ResnetData\dog_224x224\dog_224x224.txt"
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    dlist = lines[0].split(',')
+    dfloat = [float(d) for d in dlist]
+    
+    afeat = np.array(dfloat).reshape(1, -1)
+    
+    
+    
+    imgpath = r"D:\datasets\ym\Img_ResnetData\dog_224x224\dog_224x224.jpg"
+    image = cv2.imread(imgpath)
+    patches = []
+    
+    img = image[:, :, ::-1].copy()    # the model expects RGB inputs
+    patch = ReIDEncoder.transform(img)
+    
+    patch = patch.to(device=ReIDEncoder.device)
+    
+    patches.append(patch)
+    patches = torch.stack(patches, dim=0)
+    pred = ReIDEncoder.model(patches)
+    pred[torch.isinf(pred)] = 1.0
+    bfeat = pred.cpu().data.numpy()
+    
+    
+    
+    
+    aafeat = afeat / np.linalg.norm(afeat, ord=2, axis=1, keepdims=True)
+    bbfeat = bfeat / np.linalg.norm(bfeat, ord=2, axis=1, keepdims=True)
+    
+    
+    cost_matrix = 1 - np.maximum(0.0, cdist(aafeat, bbfeat, 'cosine'))
+    
+    
+    
+    
+    print("Done!!!")
+    
+    
+    
+    
+    
+
+
+def main():
+    imgpath = r"D:\datasets\ym\Img_ResnetData\20240531-103547_0354b1cb-53fa-48de-86cd-ac3c5b127ada_6921168593576\3568800050000_0.jpeg"
+    datapath = r"D:\datasets\ym\Img_ResnetData\0_tracker_inout.data"
+    savepath = r"D:\datasets\ym\Img_ResnetData\result"
+    
+    image = cv2.imread(imgpath)
+    
+    
+    Videos = read_data_file(datapath)
+    
+    bboxes, afeats = Videos[0][0][0],  Videos[0][1][0]
+    imgs, bfeats = inference_image(image, bboxes)
+    
+    
+    aafeats = afeats / np.linalg.norm(afeats, ord=2, axis=1, keepdims=True)
+    bbfeats = bfeats / np.linalg.norm(bfeats, ord=2, axis=1, keepdims=True)
+    
+    
+    cost_matrix = 1 - np.maximum(0.0, cdist(aafeats, bbfeats, 'cosine'))
+    
+    
+    for i, img in enumerate(imgs):
+        cv2.imwrite(savepath + f"\{i}.png", img)
+    
+    
+    
+    print("Done!!!!")
+    
+    
+    
+    
+if __name__ == '__main__':    
+    main()
+    
+    # test_dog()
+
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+