60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
from PIL import Image
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torchvision.models.resnet import resnet50, ResNet50_Weights
|
|
from torchvision.transforms import transforms
|
|
|
|
# Load the pre-trained model
|
|
model = resnet50(weights=ResNet50_Weights.DEFAULT)
|
|
|
|
model.eval()
|
|
|
|
# Define the image transformations
|
|
preprocess = transforms.Compose([
|
|
transforms.Resize(256),
|
|
transforms.CenterCrop(224),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
|
])
|
|
|
|
|
|
def is_cat(image_path):
|
|
# Open the image
|
|
img = Image.open(image_path)
|
|
|
|
# Preprocess the image
|
|
img_t = preprocess(img)
|
|
batch_t = torch.unsqueeze(img_t, 0)
|
|
|
|
# Make the prediction
|
|
out = model(batch_t)
|
|
|
|
# Apply softmax to get probabilities
|
|
probabilities = F.softmax(out, dim=1)
|
|
|
|
# Get the maximum predicted class and its probability
|
|
max_prob, max_class = torch.max(probabilities, dim=1)
|
|
max_prob = max_prob.item()
|
|
max_class = max_class.item()
|
|
|
|
# Check if the maximum predicted class is within the range 281-285
|
|
if 281 <= max_class <= 285:
|
|
return max_class, max_prob
|
|
else:
|
|
return max_class, None
|
|
|
|
|
|
image_path = 'wolf.jpg'
|
|
max_class, max_prob = is_cat(image_path)
|
|
translator = {
|
|
281: "tabby cat",
|
|
282: "tiger cat",
|
|
283: "persian cat",
|
|
284: "siamese cat",
|
|
285: "egyptian cat"
|
|
}
|
|
if max_prob is not None:
|
|
print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
|
|
else:
|
|
print(f"The image is not recognized as a class within the range 281-285 ({max_class})")
|