minor changes

keras_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c72459c9ab862eb75b5d073e54b34dd04e81c1fc117aa64dcff248c97e97840
+size 2453432

labels.txt

@@ -0,0 +1,2 @@
+0 pothole
+1 notpothole

model/pothole_model.py

@@ -1,67 +1,44 @@
-# from ultralyticsplus import YOLO, render_result
-# from PIL import Image
-# import numpy as np
+from keras.models import load_model  # TensorFlow is required for Keras to work
+from PIL import Image, ImageOps  # Install pillow instead of PIL
+import numpy as np
 
-# def load_model(image):
-#     # image_bytes = image.content
-#     model = YOLO('keremberke/yolov8n-pothole-segmentation')
-#     model.overrides['conf'] = 0.25
-#     model.overrides['iou'] = 0.45
-#     model.overrides['agnostic_nms'] = False
-#     model.overrides['max_det'] = 1000
 
-#     # Load image using PIL
-#     image = Image.open((image))
-#     image_array = np.array(image)
-#     # pil_image = pil_image.convert("RGB")  # Ensure image is in RGB format
+def load_image_model(image):
+    np.set_printoptions(suppress=True)
 
-#     # Convert PIL image to bytes
-#     # with io.BytesIO() as output:
-#     #     pil_image.save(output, format='JPEG')
-#     #     image_bytes = output.getvalue()
+    # Load the model
+    model = load_model("keras_Model.h5", compile=False)
 
-#     results = model.predict(image_array)
-#     for result in results:
-#         boxes = result.boxes.xyxy
-#         conf = result.boxes.conf
-#         cls = result.boxes.cls
-#         obj_info = []
-#         for i, bbox in enumerate(boxes):
-#             label = result.names[int(cls[i])]
-#             obj_info.append({
-#                 "Object": i+1,
-#                 "Label": label,
-#                 "Confidence": conf[i],
-#                 "Bounding Box": bbox
-#             })
-#     render = render_result(model=model, image=image, result=results[0])
-#     if label:
-#         print(label)
-#     render.show()
-#     return label
+    # Load the labels
+    class_names = open("labels.txt", "r").readlines()
 
+    # Create the array of the right shape to feed into the keras model
+    # The 'length' or number of images you can put into the array is
+    # determined by the first position in the shape tuple, in this case 1
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
-from PIL import Image
-from io import BytesIO
-from transformers import AutoImageProcessor, AutoModelForImageClassification
+    # Replace this with the path to your image
+    image = Image.open(image).convert("RGB")
 
-# Load model
-processor = AutoImageProcessor.from_pretrained("taroii/pothole-detection-model")
-model = AutoModelForImageClassification.from_pretrained("taroii/pothole-detection-model")
+    # resizing the image to be at least 224x224 and then cropping from the center
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
 
-# Function to predict if an image contains a pothole
-def predict_pothole(image_url):
-    image = Image.open(BytesIO(image_url))
-    inputs = processor(images=image, return_tensors="pt")
+    # turn the image into a numpy array
+    image_array = np.asarray(image)
 
-    # Perform inference
-    outputs = model(**inputs)
-    logits = outputs.logits
-    probabilities = logits.softmax(dim=1)
-    
-    # Get predicted class (0: No pothole, 1: Pothole)
-    predicted_class = probabilities.argmax().item()
-    confidence = probabilities[0, predicted_class].item()
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
 
-    return predicted_class
+    # Load the image into the array
+    data[0] = normalized_image_array
 
+    # Predicts the model
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+
+    # Print prediction and confidence score
+    print("Class:", class_name[2:], end="")
+    print("Confidence Score:", confidence_score)

requirements.txt

@@ -7,6 +7,5 @@ python-jose[cryptography]
 python-multipart
 certifi
 firebase-admin
-transformers
 pillow
-torch
+tensorflow==2.13.1