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| # ! pip install gradio | |
| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras.models import Model, load_model | |
| import numpy as np | |
| # import cv2 | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as mpatches | |
| from pathlib import Path | |
| current_directory_path = Path(__file__).parent.resolve() | |
| object_detection_model_path = current_directory_path / "carla-image-segmentation-model.h5" | |
| lane_detection_model_path = current_directory_path / "lane-detection-for-carla-model.h5" | |
| label_map_object = {0: 'Unlabeled', 1: 'Building', 2: 'Fence', 3: 'Other', | |
| 4: 'Pedestrian', 5: 'Pole', 6: 'RoadLine', 7: 'Road', 8: 'SideWalk', | |
| 9: 'Vegetation', 10: 'Vehicles', 11: 'Wall', 12: 'TrafficSign'} | |
| lane_label_map = {0: 'Unlabeled', 1: 'Left Lane', 2: 'Right Lane'} | |
| # Load the object detection model | |
| object_detection_model = load_model(object_detection_model_path) | |
| # Load the lane detection model | |
| lane_detection_model = load_model(lane_detection_model_path) | |
| def create_mask(object_detection_model, lane_detection_model, image): | |
| # tensor = tf.convert_to_tensor(image, dtype=tf.float32) | |
| image = tf.io.read_file(image.name) | |
| image = tf.image.decode_png(image, channels=3) | |
| image = tf.image.convert_image_dtype(image, tf.float32) | |
| tensor = tf.image.resize(image, (256, 256), method='nearest') | |
| # convert to tensor (specify 3 channels explicitly since png files contains additional alpha channel) | |
| # set the dtypes to align with pytorch for comparison since it will use uint8 by default | |
| # tensor = tf.io.decode_image(image_tensor, channels=3, dtype=tf.float32) | |
| # resize tensor to 224 x 224 | |
| # tensor = tf.image.resize(tensor, [256, 256]) | |
| # add another dimension at the front to get NHWC shape | |
| input_tensor = tf.expand_dims(tensor, axis=0) | |
| # with mp_selfie.SelfieSegmentation(model_selection=0) as model: | |
| # Create Masks for with Object Detection Model | |
| pred_masks_object_detect = object_detection_model.predict(input_tensor) | |
| pred_masks_object_detect = tf.expand_dims(tf.argmax(pred_masks_object_detect, axis=-1), axis=-1) | |
| pred_masks_object_detect = np.array(pred_masks_object_detect) | |
| # Create Masks for with Lane Detection Model | |
| pred_masks_lane_detect = lane_detection_model.predict(input_tensor) | |
| pred_masks_lane_detect = tf.expand_dims(tf.argmax(pred_masks_lane_detect, axis=-1), axis=-1) | |
| pred_masks_lane_detect = np.array(pred_masks_lane_detect) | |
| return pred_masks_object_detect, pred_masks_lane_detect | |
| def segment_object(image): | |
| pred_masks_object_detect, pred_masks_lane_detect = create_mask(object_detection_model, lane_detection_model, image) | |
| # image = cv2.resize(image, dsize=(256, 256), interpolation=cv2.INTER_CUBIC) | |
| used_classes_object = np.unique(pred_masks_object_detect[0]) | |
| used_classes_lane = np.unique(pred_masks_lane_detect[0]) | |
| fig_object = plt.figure() | |
| im = plt.imshow(tf.keras.preprocessing.image.array_to_img(pred_masks_object_detect[0])) | |
| patches_1 = [mpatches.Patch(color=im.cmap(im.norm(int(cls))), label="{}".format(label_map_object[int(cls)])) for cls in used_classes_object] | |
| # put those patched as legend-handles into the legend | |
| plt.legend(handles=patches_1, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) | |
| plt.axis("off") | |
| fig_lane = plt.figure() | |
| im = plt.imshow(tf.keras.preprocessing.image.array_to_img(pred_masks_lane_detect[0])) | |
| patches_1 = [mpatches.Patch(color=im.cmap(im.norm(int(cls))), label="{}".format(lane_label_map[int(cls)])) for cls in used_classes_lane] | |
| # put those patched as legend-handles into the legend | |
| plt.legend(handles=patches_1, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) | |
| plt.axis("off") | |
| return fig_object | |
| webcam = gr.inputs.Image(shape=(800, 600), source="upload", type='file') #upload | |
| webapp = gr.interface.Interface(fn=segment_object, inputs=webcam, outputs="plot") #, live=False | |
| webapp.launch(debug=True) | |