import numpy as np
import cv2

from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
from PIL import Image

def grad_cam(model, img, cls):
    model.eval()
    img = np.array(img)
    img = cv2.resize(img, (224, 224))
    img = np.float32(img) / 255
    input_tensor = preprocess_image(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

    # The target for the CAM is the Bear category.
    # As usual for classication, the target is the logit output
    # before softmax, for that category.
    targets = [ClassifierOutputTarget(1)]
    target_layers = [model.layer4[-1]]
    with GradCAM(model=model, target_layers=target_layers) as cam:
        grayscale_cams = cam(input_tensor=input_tensor, targets=targets)
        cam_image = show_cam_on_image(img, grayscale_cams[0, :], use_rgb=True)
    cam = np.uint8(255*grayscale_cams[0, :])
    cam = cv2.merge([cam, cam, cam])
    return Image.fromarray(cam_image)