# -*- coding: utf-8 -*- """ Created on Sat Dec 3 18:31:26 2022 @author: gabri """ import numpy as np import tensorflow as tf import gradio as gr from huggingface_hub import from_pretrained_keras import cv2 import requests from PIL import Image import matplotlib.cm as cm # import matplotlib.pyplot as plt models_links = { 'xception':r'https://huggingface.co/gabri14el/grapevine_classification/resolve/main/experimentos/classificacao/Experimento%205/pesos.h5', 'resnet':r'https://huggingface.co/gabri14el/grapevine_classification/resolve/main/experimentos/classificacao/Experimento%209/pesos.h5', 'efficientnet':'https://huggingface.co/gabri14el/grapevine_classification/resolve/main/experimentos/classificacao/Experimento%2010/pesos.h5'} model_weights = { } model_last_convolutional_layer = { 'xception':'block14_sepconv2_act', 'resnet':'conv5_block3_3_conv', 'efficientnet':'top_conv'} classes = ['Códega', 'Moscatel Galego', 'Rabigato', 'Tinta Roriz', 'Tinto Cao', 'Touriga Nacional'] # functions for inference target_size_dimension = 300 def define_model(model): weights = get_weights(model) if model == 'efficientnet': preprocessing_function=tf.keras.applications.efficientnet.preprocess_input model = tf.keras.applications.EfficientNetB3( include_top=False, input_shape= (target_size_dimension, target_size_dimension, 3), weights='imagenet', pooling='avg' ) elif model == 'resnet': preprocessing_function=tf.keras.applications.resnet_v2.preprocess_input model = tf.keras.applications.resnet_v2.ResNet101V2( include_top=False, input_shape= (target_size_dimension, target_size_dimension, 3), weights='imagenet', pooling='avg' ) else: preprocessing_function=tf.keras.applications.xception.preprocess_input model = tf.keras.applications.Xception( include_top=False, input_shape= (target_size_dimension, target_size_dimension, 3), weights='imagenet', pooling='avg' ) x = tf.keras.layers.Dense(512, activation='relu')(model.output) x = tf.keras.layers.Dropout(0.25)(x) x = tf.keras.layers.Dense(512, activation='relu')(x) x = tf.keras.layers.Dropout(0.25)(x) output = tf.keras.layers.Dense(6, activation='softmax')(x) nmodel = tf.keras.models.Model(model.input, output) nmodel.load_weights(weights) return preprocessing_function, nmodel def get_weights(model): if not model in model_weights: r = requests.get(models_links[model], allow_redirects=True) open(model+'.h5', 'wb').write(r.content) model_weights[model] = model+'.h5' return model_weights[model] def get_img_array(img_path, size, expand=True): # `img` is a PIL image of size 299x299 img = tf.keras.preprocessing.image.load_img(img_path, target_size=size) # `array` is a float32 Numpy array of shape (299, 299, 3) array = tf.keras.preprocessing.image.img_to_array(img) # We add a dimension to transform our array into a "batch" # of size (1, 299, 299, 3) if expand: array = np.expand_dims(array, axis=0) return array def make_gradcam_heatmap(img_array, grad_model, last_conv_layer_name, pred_index=None, tresh=0.1): # 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) heatmap = heatmap.numpy() return heatmap def save_and_display_gradcam(img, heatmap, cam_path="cam.jpg", alpha=0.4): # Rescale heatmap to a range 0-255 heatmap = np.uint8(255 * heatmap) im = Image.fromarray(heatmap) im = im.resize((img.shape[1], img.shape[0])) im = np.asarray(im) im = np.where(im > 0, 1, im) # 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 = tf.keras.preprocessing.image.array_to_img(jet_heatmap) jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0])) jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap) # Superimpose the heatmap on original image superimposed_img = jet_heatmap * alpha + img superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img) # Save the superimposed image #superimposed_img.save(cam_path) # Display Grad CAM #display(Image(cam_path)) return superimposed_img, im def infer(model_name, input_image): print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$') print(model_name, type(input_image)) preprocess, model = define_model(model_name) #img = get_img_array(input_image, (target_size_dimension, target_size_dimension)) img_processed = preprocess(np.expand_dims(input_image, axis=0)) predictions = model.predict(img_processed) predictions = np.squeeze(predictions) result = {} for i in range(len(classes)): result[classes[i]] = float(predictions[i]) #predictions = np.argmax(predictions) # , axis=2 #predicted_label = classes[predictions.item()] print(input_image.shape) model.layers[-1].activation = None grad_model = tf.keras.models.Model([model.inputs], [model.get_layer(model_last_convolutional_layer[model_name]).output, model.output]) print(result) heatmap = make_gradcam_heatmap(img_processed, grad_model,model_last_convolutional_layer[model_name]) heat, mask = save_and_display_gradcam(input_image, heatmap) return result, heat gr.outputs.Image() # get the inputs css = css = ".output-image, .input-image, .image-preview {height: 300px !important}" inputs = [gr.Radio(["resnet", "efficientnet", "xception"], label='Choose a model'), gr.inputs.Image(shape=(target_size_dimension, target_size_dimension), label='Select an image')] # the app outputs two segmented images output = [gr.outputs.Label(label="Result"), gr.outputs.Image(type="numpy", label="Heatmap (Grad-CAM)")] # it's good practice to pass examples, description and a title to guide users examples = [["./content/examples/Frog.jpg"], ["./content/examples/Truck.jpg"]] title = "Grapevine image classification" description = "Upload an image to classify it. The allowed classes are - Códega, Moscatel Galego, Rabigato, Tinta Roriz, Tinto Cao, Touriga Nacional

Space author: Gabriel Carneiro
gabri14el@gmail.com

" gr_interface = gr.Interface(infer, inputs, output, allow_flagging=False, analytics_enabled=False, css=css, title=title, description=description).launch(enable_queue=True, debug=False) #gr_interface.launch()