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import torch
from PIL import Image
from torchvision import transforms
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
model = torch.hub.load(r"ultralytics/yolov5", 'custom', path=r'./models/best.pt')
img_path = r"ACNE.jpg"
image = Image.open(img_path)
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize to model's expected input size
transforms.ToTensor(), # Convert image to tensor
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Normalize
])
im = transform(image).unsqueeze(0) # Add batch dimension (BCHW)
try:
output = model(im)
print(output)
except Exception as e:
logger.error(f"Error in image prediction: {e}")
# Get predictions
with torch.no_grad():
output = model(im) # Raw model output (logits)
# Apply softmax to get confidence scores
softmax = torch.nn.Softmax(dim=1)
probs = softmax(output)
# Get the predicted class and its confidence score
predicted_class_id = torch.argmax(probs, dim=1).item()
confidence_score = probs[0, predicted_class_id].item()
# Print predicted class and confidence score
print(f"Predicted Class ID: {predicted_class_id}")
print(f"Confidence Score: {confidence_score:.4f}")
# Print predicted class name if available
if hasattr(model, 'names'):
class_name = model.names[predicted_class_id]
print(f"Predicted Class Name: {class_name}")
# import torch
# import cv2 # Import OpenCV
# from torchvision import transforms
# import pathlib
# # Pathlib adjustment for Windows compatibility
# temp = pathlib.PosixPath
# pathlib.PosixPath = pathlib.WindowsPath
# # Load pre-trained YOLOv5 model
# model = torch.hub.load(
# r'C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\yolov5',
# 'custom',
# path=r"C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\models\best.pt",
# source="local"
# )
# # Set model to evaluation mode
# model.eval()
# # Define image transformations (for PyTorch)
# transform = transforms.Compose([
# transforms.ToTensor(), # Convert image to tensor
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Normalize
# ])
# # Load and preprocess the image using OpenCV
# img_path = r"C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\ACNE.jpg"
# image = cv2.imread(img_path) # Load image in BGR format
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
# image_resized = cv2.resize(image, (224, 224)) # Resize to match model's expected input size
# # Transform the image for the model
# im = transform(image_resized).unsqueeze(0) # Add batch dimension (BCHW)
# # Get predictions
# with torch.no_grad():
# output = model(im) # Raw model output (logits)
# # Apply softmax to get confidence scores
# softmax = torch.nn.Softmax(dim=1)
# probs = softmax(output)
# # Get the predicted class and its confidence score
# predicted_class_id = torch.argmax(probs, dim=1).item()
# confidence_score = probs[0, predicted_class_id].item()
# # Print predicted class and confidence score
# print(f"Predicted Class ID: {predicted_class_id}")
# print(f"Confidence Score: {confidence_score:.4f}")
# # Print predicted class name if available
# if hasattr(model, 'names'):
# class_name = model.names[predicted_class_id]
# print(f"Predicted Class Name: {class_name}")
# cv2.imshow("Input Image", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
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