sketch/sketch.py

import torch
from PIL import Image
import numpy as np
from torch import nn
from pathlib import Path

PATH="sketch/class_names.txt"
LABELS = Path(PATH).read_text().splitlines()

model = nn.Sequential(
    nn.Conv2d(1, 32, 3, padding='same'),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(32, 64, 3, padding='same'),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(64, 128, 3, padding='same'),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Flatten(),
    nn.Linear(1152, 256),
    nn.ReLU(),
    nn.Linear(256, len(LABELS)),
)
state_dict = torch.load('sketch/pytorch_model.bin',    map_location='cpu')
model.load_state_dict(state_dict, strict=False)
model.eval()

def Predict(img):
    # Convert to grayscale if the image is RGB
    if img is not None and img.any():
        if img.shape[-1] == 3:
            img = np.mean(img, axis=-1)
    # Convert the NumPy array to a PIL image
    img_pil = Image.fromarray(img.astype('uint8'))
    # Resize the image
    img_resized = img_pil.resize((28, 28), Image.ANTIALIAS) 
    # Convert the PIL image back to a NumPy array
    img_np = np.array(img_resized)
    # Convert to tensor and normalize
    x = torch.tensor(img_np, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0
    # Model prediction
    with torch.no_grad():
        out = model(x)
    probabilities = torch.nn.functional.softmax(out[0], dim=0)
    values, indices = torch.topk(probabilities, 5)
    confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)}
    return confidences