make code more flexible

neural_network/inference.py

@@ -10,61 +10,50 @@ parser.add_argument('-i', '--input',
     help='path to the input image')
 args = vars(parser.parse_args())
 
-# the computation device
-device = ('cuda' if torch.cuda.is_available() else 'cpu')
-# list containing all the class labels
-labels = [
-    'bean', 'bitter gourd', 'bottle gourd', 'brinjal', 'broccoli',
-    'cabbage', 'capsicum', 'carrot', 'cauliflower', 'cucumber',
-    'papaya', 'potato', 'pumpkin', 'radish', 'tomato'
-    ]
+def main(path):
+    # the computation device
+    device = ('cuda' if torch.cuda.is_available() else 'cpu')
+    # list containing all the class labels
+    labels = [
+        'bean', 'bitter gourd', 'bottle gourd', 'brinjal', 'broccoli',
+        'cabbage', 'capsicum', 'carrot', 'cauliflower', 'cucumber',
+        'papaya', 'potato', 'pumpkin', 'radish', 'tomato'
+        ]
 
-# initialize the model and load the trained weights
-model = CNNModel().to(device)
-checkpoint = torch.load('outputs/model.pth', map_location=device)
-model.load_state_dict(checkpoint['model_state_dict'])
-model.eval()
+    # initialize the model and load the trained weights
+    model = CNNModel().to(device)
+    checkpoint = torch.load('outputs/model.pth', map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
 
-# define preprocess transforms
-transform = transforms.Compose([
-    transforms.ToPILImage(),
-    transforms.Resize(224),
-    transforms.ToTensor(),
-    transforms.Normalize(
-        mean=[0.5, 0.5, 0.5],
-        std=[0.5, 0.5, 0.5]
-    )
-])  
+    # define preprocess transforms
+    transform = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize(224),
+        transforms.ToTensor(),
+        transforms.Normalize(
+            mean=[0.5, 0.5, 0.5],
+            std=[0.5, 0.5, 0.5]
+        )
+    ])  
 
 
-# read and preprocess the image
-image = cv2.imread(args['input'])
-# get the ground truth class
-gt_class = args['input'].split('/')[-2]
-orig_image = image.copy()
-# convert to RGB format
-image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-image = transform(image)
-# add batch dimension
-image = torch.unsqueeze(image, 0)
-with torch.no_grad():
-    outputs = model(image.to(device))
-output_label = torch.topk(outputs, 1)
-pred_class = labels[int(output_label.indices)]
-cv2.putText(orig_image, 
-    f"GT: {gt_class}",
-    (10, 25),
-    cv2.FONT_HERSHEY_SIMPLEX, 
-    0.6, (0, 255, 0), 2, cv2.LINE_AA
-)
-cv2.putText(orig_image, 
-    f"Pred: {pred_class}",
-    (10, 55),
-    cv2.FONT_HERSHEY_SIMPLEX, 
-    0.6, (0, 0, 255), 2, cv2.LINE_AA
-)
-print(f"GT: {gt_class}, pred: {pred_class}")
-cv2.imshow('Result', orig_image)
-cv2.waitKey(0)
-cv2.imwrite(f"outputs/{gt_class}{args['input'].split('/')[-1].split('.')[0]}.png",
-    orig_image)
+    # read and preprocess the image
+    image = cv2.imread(path)
+    # get the ground truth class
+    gt_class = path.split('/')[-2]
+    orig_image = image.copy()
+    # convert to RGB format
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = transform(image)
+    # add batch dimension
+    image = torch.unsqueeze(image, 0)
+    with torch.no_grad():
+        outputs = model(image.to(device))
+    output_label = torch.topk(outputs, 1)
+    pred_class = labels[int(output_label.indices)]
+    
+    return pred_class
+
+if __name__ == "__main__":
+    main(args['input'])