prediction.py

from main import *
from deepface import DeepFace

# Checking for all types of devices available
if torch.backends.mps.is_available():
    device = "mps"
elif torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

print(f"Using device: {device}")
# Predicting the model
# def prediction(model, image_path):
model = pyramid_trans_expr2(img_size=224, num_classes=7)

model = torch.nn.DataParallel(model)
model = model.to(device)

model_path = "raf-db-model_best.pth"
image_arr = []
for foldername, subfolders, filenames in os.walk(
    "/Users/futuregadgetlab/Downloads/Testing/"
):
    for filename in filenames:
        # Construct the full path to the file
        file_path = os.path.join(foldername, filename)
        image_arr.append(f"{file_path}")


def main():
    if model_path is not None:
        if os.path.isfile(model_path):
            print("=> loading checkpoint '{}'".format(model_path))
            checkpoint = torch.load(model_path, map_location=device)
            best_acc = checkpoint["best_acc"]
            best_acc = best_acc.to()
            print(f"best_acc:{best_acc}")
            model.load_state_dict(checkpoint["state_dict"])
            print(
                "=> loaded checkpoint '{}' (epoch {})".format(
                    model_path, checkpoint["epoch"]
                )
            )
        else:
            print("=> no checkpoint found at '{}'".format(model_path))
        predict(model, image_path=image_arr)
        return


def predict(model, image_path):
    from face_detection import face_detection

    with torch.no_grad():
        transform = transforms.Compose(
            [
                transforms.Resize((224, 224)),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
                transforms.RandomErasing(p=1, scale=(0.05, 0.05)),
            ]
        )
        face = face_detection(image_path)
        image_tensor = transform(face).unsqueeze(0)
        image_tensor = image_tensor.to(device)

        model.eval()
        img_pred = model(image_tensor)
        topk = (3,)
        with torch.no_grad():
            maxk = max(topk)
            # batch_size = target.size(0)
            _, pred = img_pred.topk(maxk, 1, True, True)
            pred = pred.t()

        img_pred = pred
        img_pred = img_pred.squeeze().cpu().numpy()
        im_pre_label = np.array(img_pred)
        y_pred = im_pre_label.flatten()
        emotions = {
            0: "Surprise",
            1: "Fear",
            2: "Disgust",
            3: "Happy",
            4: "Sad",
            5: "Angry",
            6: "Neutral",
        }
        labels = []
        for i in y_pred:
            labels.append(emotions.get(i))

        print(
            f"-->Image Path {image_path} [!] The predicted labels are {y_pred} and the label is {labels}"
            )
    return


if __name__ == "__main__":
    main()