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import utils + +def make_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() + + +def make_gradcam(img, heatmap, alpha=0.5): + # convert img to float32 to support alpha blending + img = tf.image.convert_image_dtype(img, dtype=tf.float32) + + # Rescale heatmap to a range 0-255 + heatmap = np.uint8(255 * heatmap) + + # Use jet colormap to colorize heatmap + jet = plt.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) + jet_heatmap = jet_heatmap / 255 + + # Superimpose the heatmap on original image + superimposed_img = jet_heatmap * alpha + img * (1 - alpha) + superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img) + + # Display Grad CAM + return superimposed_img + + RESIZED_SIZE = (100, 50, 3) LABELS = ["octane", "werewolf", "breakout", "aftershock"] MODELS_PATH = "models" -MODEL_FILENAME = "rot_25e" +MODEL_FILENAME = "full_aug_5e" + +last_conv_layer_name = "C2D_last" # Load model model = models.load_model(MODELS_PATH + "/" + MODEL_FILENAME) utils.startup(need_focus=False) +utils.screenshot(filename="live", folder=MODELS_PATH) + +# Attendre que la première image soit créée +time.sleep(10) running = True X = np.zeros((1, RESIZED_SIZE[1], RESIZED_SIZE[0], RESIZED_SIZE[2])) -while running: - utils.screenshot(filename="live", folder=MODELS_PATH) +plt.ion() +plt.show() - # time.sleep(1) +while running: + + utils.screenshot(filename="live", folder=MODELS_PATH) # Lecture de l'image img = PIL.Image.open(MODELS_PATH + "/live.jpg") @@ -40,11 +110,28 @@ while running: X[0] = np.asarray(img) - Y = model.predict(X) - - index = int(np.dot(Y, np.array([0, 1, 2, 3]).T)) + preds = model.predict(X) + index = np.argmax(preds) os.system("clear") print(f"Model detected : {LABELS[index]}") for i in range(len(LABELS)): - print(f"\t- {LABELS[i]} {Y[0,i]:.03f}") + print(f"\t- {LABELS[i]} {preds[0,i]:.03f}") + + plt.subplot(1, 5, 1) + plt.imshow(img) + plt.title(f"prediction: {LABELS[index]}") + + for i in range(4): + # generate class activation heatmap + heatmap = make_heatmap(X, model, last_conv_layer_name, pred_index=i) + # generate gradmap + gradcam = make_gradcam(img, heatmap) + + plt.subplot(1, 5, i + 2) + plt.imshow(gradcam) + plt.title(f"{LABELS[i]} ({preds[0][i]:.4f})") + + plt.tight_layout() + plt.draw() + plt.pause(0.001)