# -*- coding: utf-8 -*-
"""
Created on Tue Dec 27 08:48:25 2022

@author: Usuario
"""

from keras.models import load_model
import tensorflow as tf
from tensorflow.keras.utils import load_img, img_to_array, array_to_img
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg19 import preprocess_input, decode_predictions
import matplotlib.pyplot as plt

import numpy as np
from IPython.display import Image, display
import matplotlib.cm as cm

#http://gradcam.cloudcv.org/
#https://keras.io/examples/vision/grad_cam/

def get_img_array(img_path, size):
    # `img` is a PIL image of size 299x299
    img = load_img(img_path, target_size=size)
    # `array` is a float32 Numpy array of shape (299, 299, 3)
    array = img_to_array(img)
    # We add a dimension to transform our array into a "batch"
    # of size (1, 299, 299, 3)
    array = np.expand_dims(array, axis=0)
    return array

def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
    # First, we create a model that maps the input image to the activations
    # of the last conv layer as well as the output predictions
    grad_model = tf.keras.models.Model(
        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
    )

    # Then, we compute the gradient of the top predicted class for our input image
    # with respect to the activations of the last conv layer
    with tf.GradientTape() as tape:
        last_conv_layer_output, preds = grad_model(img_array)
        if pred_index is None:
            pred_index = tf.argmax(preds[0])
        class_channel = preds[:, pred_index]

    # This is the gradient of the output neuron (top predicted or chosen)
    # with regard to the output feature map of the last conv layer
    grads = tape.gradient(class_channel, last_conv_layer_output)

    # This is a vector where each entry is the mean intensity of the gradient
    # over a specific feature map channel
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))

    # We multiply each channel in the feature map array
    # by "how important this channel is" with regard to the top predicted class
    # then sum all the channels to obtain the heatmap class activation
    last_conv_layer_output = last_conv_layer_output[0]
    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
    heatmap = tf.squeeze(heatmap)

    # For visualization purpose, we will also normalize the heatmap between 0 & 1
    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
    
    return heatmap.numpy()



# Generate class activation heatmap
#heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)


def save_and_display_gradcam(img_path, heatmap, alpha = 0.4):
    # Load the original image
    img = load_img(img_path)
    img = img_to_array(img)

    # Rescale heatmap to a range 0-255
    heatmap = np.uint8(255 * heatmap)

    # Use jet colormap to colorize heatmap
    jet = cm.get_cmap("jet")

    # Use RGB values of the colormap
    jet_colors = jet(np.arange(256))[:, :3]
    jet_heatmap = jet_colors[heatmap]

    # Create an image with RGB colorized heatmap
    jet_heatmap = array_to_img(jet_heatmap)
    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
    jet_heatmap = img_to_array(jet_heatmap)

    # Superimpose the heatmap on original image
    superimposed_img = jet_heatmap * alpha + img
    superimposed_img = array_to_img(superimposed_img)

    # Save the superimposed image
    #superimposed_img.save('')

    # Display Grad CAM
    return superimposed_img
    #display(Image(superimposed_img))
    
#save_and_display_gradcam(path_image+name_image, heatmap)