modified for site test

featureVal.py

@@ -104,25 +104,76 @@ def inference_image(image, detections):
 
     return imgs, features
 
-def test_dog():
+def readimg():
+    imgpath = r"D:\datasets\ym\Img_ResnetData\result\0.png"
+    image = cv2.imread(imgpath)
     
+    img = cv2.resize(image, (224, 224))
+    
+    cv2.imwrite('0_224x224.jpg', img)
+    
+
+def readdata(datapath):
     
-    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)
     
+    return afeat
+def readrawimg(datapath):
+    with open(datapath, 'r') as file:  
+        llines = file.readlines()
+    imgs = []
+    
+    row = 224
+    
+    for i in range(8):
+        lines = llines[i*224 : (i+1)*224]
+        
     
     
-    imgpath = r"D:\datasets\ym\Img_ResnetData\dog_224x224\dog_224x224.jpg"
-    image = cv2.imread(imgpath)
+        img = np.empty((224, 224, 0), dtype=np.float32)
+        imgr = np.empty((0, 224), dtype=np.float32)
+        imgg = np.empty((0, 224), dtype=np.float32) 
+        imgb = np.empty((0, 224), dtype=np.float32)
+        
+        for line in lines:
+            dlist = line.split(' ')[0:224]
+            
+            img_r = np.array([float(s.split(',')[0]) for s in dlist], dtype=np.float32).reshape(1, -1)
+            img_g = np.array([float(s.split(',')[1]) for s in dlist], dtype=np.float32).reshape(1, -1)
+            img_b = np.array([float(s.split(',')[2]) for s in dlist], dtype=np.float32).reshape(1, -1)
+            
+            # img_r = [float(s.split(',')[0]) for s in dlist if len(s.split(',')[0].encode('utf-8')) == 4]
+            # img_g = [float(s.split(',')[1]) for s in dlist if len(s.split(',')[1].encode('utf-8')) == 4]
+            # img_b = [float(s.split(',')[2]) for s in dlist if len(s.split(',')[2].encode('utf-8')) == 4]
+            
+            imgr = np.concatenate((imgr, img_r), axis=0)
+            imgg = np.concatenate((imgg, img_g), axis=0)
+            imgb = np.concatenate((imgb, img_b), axis=0)
+        
+        imgr = imgr[:, :, None]
+        imgg = imgg[:, :, None]
+        imgb = imgb[:, :, None]
+    
+        img = np.concatenate((imgb, imgg, imgr), axis=2).astype(np.uint8)
+        
+        imgs.append(img)
+
+    return imgs
+    
+
+
+
+
+def inference(image):
+    
     patches = []
     
-    img = image[:, :, ::-1].copy()    # the model expects RGB inputs
-    patch = ReIDEncoder.transform(img)
+    image = image[:, :, ::-1].copy()    # the model expects RGB inputs
+    patch = ReIDEncoder.transform(image)
     
     patch = patch.to(device=ReIDEncoder.device)
     
@@ -132,29 +183,43 @@ def test_dog():
     pred[torch.isinf(pred)] = 1.0
     bfeat = pred.cpu().data.numpy()
     
+    return bfeat
+    
+
+
+def test_img_feat():
+    # datapath = r"D:\datasets\ym\Img_ResnetData\aa\aa.txt"
+    # afeat = readdata(datapath)
+
+    imgpath = r"D:\datasets\ym\Img_ResnetData\aa\aa.jpg"
+    img = cv2.imread(imgpath)
+    bfeat = inference(img)
     
     
+    datapath = r"D:\datasets\ym\Img_ResnetData\rawimg\7.txt"
+    afeat = readdata(datapath)
+
+    rawpath = r"D:\datasets\ym\Img_ResnetData\rawimg\28950640607_mat_rgb"
+    imgx = readrawimg(rawpath)
+    cv2.imwrite("rawimg.png", imgx[7])
+    bfeatx = inference(imgx[7])
+    
+    cost_matrix = 1 - np.maximum(0.0, cdist(afeat, bfeatx, 'cosine'))
+    
+    imgpath1 = r"D:\datasets\ym\Img_ResnetData\result\0_224x224.png"
+    img1 = cv2.imread(imgpath1)
+    bfeat1 = inference(img1)
     
     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!!!")
-    
-    
-    
-    
-    
 
+    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"
+    datapath = r"D:\datasets\ym\Img_ResnetData\20240531-103547_0354b1cb-53fa-48de-86cd-ac3c5b127ada_6921168593576\0_tracker_inout.data"
     savepath = r"D:\datasets\ym\Img_ResnetData\result"
     
     image = cv2.imread(imgpath)
@@ -184,9 +249,11 @@ def main():
     
     
 if __name__ == '__main__':    
-    main()
+    # main()
     
-    # test_dog()
+    # readimg()
+    
+    test_img_feat()