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import tensorflow as tf
import numpy as np
from tensorflow import keras
from keras.layers import Input, Lambda, Dense, Flatten, Rescaling
from keras.models import Model
import PIL
from PIL import Image
import gradio as gr
base_model = keras.applications.Xception(
# weights = "../input/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
input_shape = (160,160,3),
include_top = False,)
base_model.trainable = False
#function for creating model
#returns model, its inputs, Xception's last conv output, the whole model's outputs
def create_model_mod():
inputs = keras.Input(shape = (160,160,3))
#normalizing pixel values
r = Rescaling(scale = 1./255)(inputs)
x = base_model(r, training = False)
gap = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(1,activation = 'linear')(gap)
model = keras.Model(inputs, outputs)
model.compile(
loss = keras.losses.BinaryCrossentropy(from_logits = True),
optimizer = keras.optimizers.Adam(0.001),
metrics = ["accuracy"]
)
return model, inputs, x, outputs
def create_heatmap(model, imgs):
#predicting the images and getting the conv outputs and predictions
with tf.GradientTape() as tape:
maps, preds = model(imgs);
#computing gradients of predictions w.r.t the feature maps
grads = tape.gradient(preds, maps)
# global average pooling of each feature map
gap_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
#multiplying each pooled value with its correponding feature map
# maps = maps[0]
heatmap = maps @ gap_grads[..., tf.newaxis]
#removing the extra dimension of value 1
heatmap = tf.squeeze(heatmap)
#applying relu activation
heatmap = tf.keras.activations.relu(heatmap)
return heatmap, preds.numpy()
def superimpose_single(heatmap, img, alpha = 0.4):
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 = keras.utils.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((160,160))
jet_heatmap = keras.utils.img_to_array(jet_heatmap)
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img
# superimposed_img = keras.utils.array_to_img(superimposed_img)
return superimposed_img
def gen_grad_img_single(weights, img, alpha = 0.4):
model_mod, input, x, output = create_model_mod()
model_mod.load_weights(weights)
grad_model = Model(input, [x, output])
heatmaps, y_pred = create_heatmap(grad_model, img)
for i in range(len(y_pred)):
if y_pred[i] > 0.5: y_pred[i] = 1
else: y_pred[i] = 0
img = superimpose_single(heatmaps, img[0])
return np.array(img).astype('uint8'), y_pred
weights = "weights.h5"
# img, y_pred = gen_grad_img_single(weights, img)
def get_grad(img):
a = gen_grad_img_single(weights, img)
return a
demo = gr.Interface(
fn = get_grad,
inputs = gr.Image(type = "pil", shape = (224,224)),
# outputs = [gr.outputs.Label(num_top_classes = 2, label = 'Classifiaction'), gr.Textbox('infer_time', label = 'Inference Time(ms)')]
outputs = [gr.Image(type = "numpy"), gr.Textbox('y_pred', label = 'Prediction')]
)
demo.launch()
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