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| # from PIL import Image | |
| # from io import BytesIO | |
| # from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| # # Load model | |
| # processor = AutoImageProcessor.from_pretrained("taroii/pothole-detection-model") | |
| # model = AutoModelForImageClassification.from_pretrained("taroii/pothole-detection-model") | |
| # # 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") | |
| # # 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() | |
| # return predicted_class | |
| import tensorflow as tf | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| import requests | |
| from io import BytesIO | |
| from keras.models import load_model | |
| def load_image_model(image): | |
| # Disable scientific notation for clarity | |
| np.set_printoptions(suppress=True) | |
| # Load the model from the URL | |
| model = load_model("keras_model.h5", compile=False) | |
| # 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) | |
| # Replace this with the path to your image | |
| image = Image.open(BytesIO(image)).convert("RGB") | |
| # 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) | |
| # turn the image into a numpy array | |
| image_array = np.asarray(image) | |
| # Normalize the image | |
| normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1 | |
| # 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) | |
| return class_name |