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import os
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from handetect.models import *
from torchmetrics import ConfusionMatrix
import matplotlib.pyplot as plt
# Define the path to your model checkpoint
model_checkpoint_path = "model.pth"
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NUM_CLASSES = 6
# Define transformation for preprocessing the input image
preprocess = transforms.Compose(
[
transforms.Resize((64, 64)), # Resize the image to match training input size
transforms.Grayscale(num_output_channels=3), # Convert the image to grayscale
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # Normalize the image
]
)
# Load your model (change this according to your model definition)
model = squeezenet1_0(pretrained=False, num_classes=NUM_CLASSES)
model.load_state_dict(
torch.load(model_checkpoint_path, map_location=DEVICE)
) # Load the model on the same device
model.eval()
model = model.to(DEVICE)
model.eval()
torch.set_grad_enabled(False)
def predict_image(image_path, model=model, transform=preprocess):
# Define images variable to recursively list all the data file in the image_path
classes = ['Cerebral Palsy', 'Dystonia', 'Essential Tremor', 'Healthy', 'Huntington Disease', 'Parkinson Disease']
print("---------------------------")
print("Image path:", image_path)
image = Image.open(image_path)
image = transform(image).unsqueeze(0)
image = image.to(DEVICE)
output = model(image)
# softmax algorithm
probabilities = torch.softmax(output, dim=1)[0] * 100
# Sort the classes by probabilities in descending order
sorted_classes = sorted(
zip(classes, probabilities), key=lambda x: x[1], reverse=True
)
# Report the prediction for each class
print("Probabilities for each class:")
for class_label, class_prob in sorted_classes:
class_prob = class_prob.item().__round__(2)
print(f"{class_label}: {class_prob}%")
# Get the predicted class
predicted_class = sorted_classes[0][0] # Most probable class
predicted_label = classes.index(predicted_class)
# Report the prediction
print("Predicted class:", predicted_label)
print("Predicted label:", predicted_class)
print("---------------------------")
return sorted_classes
# # Call the predict_image function
# predicted_label, sorted_probabilities = predict_image(image_path, model, preprocess)
# # Access probabilities for each class in sorted order
# for class_label, class_prob in sorted_probabilities:
# print(f"{class_label}: {class_prob}%")
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