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Runtime error
David
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Parent(s):
c23593f
Add application files
Browse files- app.py +98 -0
- carla-image-segmentation-model.h5 +3 -0
- lane-detection-for-carla-model.h5 +3 -0
- requirements.txt +6 -0
app.py
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# ! pip install gradio
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import gradio as gr
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.models import Model, load_model
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import numpy as np
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# import cv2
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from PIL import Image
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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from pathlib import Path
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current_directory_path = Path(__file__).parent.resolve()
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object_detection_model_path = current_directory_path / "carla-image-segmentation-model.h5"
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lane_detection_model_path = current_directory_path / "lane-detection-for-carla-model.h5"
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label_map_object = {0: 'Unlabeled', 1: 'Building', 2: 'Fence', 3: 'Other',
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4: 'Pedestrian', 5: 'Pole', 6: 'RoadLine', 7: 'Road', 8: 'SideWalk',
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9: 'Vegetation', 10: 'Vehicles', 11: 'Wall', 12: 'TrafficSign'}
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lane_label_map = {0: 'Unlabeled', 1: 'Left Lane', 2: 'Right Lane'}
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# Load the object detection model
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object_detection_model = load_model(object_detection_model_path)
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# Load the lane detection model
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lane_detection_model = load_model(lane_detection_model_path)
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def create_mask(object_detection_model, lane_detection_model, image):
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# tensor = tf.convert_to_tensor(image, dtype=tf.float32)
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image = tf.io.read_file(image.name)
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image = tf.image.decode_png(image, channels=3)
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image = tf.image.convert_image_dtype(image, tf.float32)
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tensor = tf.image.resize(image, (256, 256), method='nearest')
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# convert to tensor (specify 3 channels explicitly since png files contains additional alpha channel)
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# set the dtypes to align with pytorch for comparison since it will use uint8 by default
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# tensor = tf.io.decode_image(image_tensor, channels=3, dtype=tf.float32)
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# resize tensor to 224 x 224
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# tensor = tf.image.resize(tensor, [256, 256])
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# add another dimension at the front to get NHWC shape
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input_tensor = tf.expand_dims(tensor, axis=0)
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# with mp_selfie.SelfieSegmentation(model_selection=0) as model:
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# Create Masks for with Object Detection Model
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pred_masks_object_detect = object_detection_model.predict(input_tensor)
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pred_masks_object_detect = tf.expand_dims(tf.argmax(pred_masks_object_detect, axis=-1), axis=-1)
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pred_masks_object_detect = np.array(pred_masks_object_detect)
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# Create Masks for with Lane Detection Model
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pred_masks_lane_detect = lane_detection_model.predict(input_tensor)
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pred_masks_lane_detect = tf.expand_dims(tf.argmax(pred_masks_lane_detect, axis=-1), axis=-1)
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pred_masks_lane_detect = np.array(pred_masks_lane_detect)
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return pred_masks_object_detect, pred_masks_lane_detect
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def segment_object(image):
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pred_masks_object_detect, pred_masks_lane_detect = create_mask(object_detection_model, lane_detection_model, image)
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# image = cv2.resize(image, dsize=(256, 256), interpolation=cv2.INTER_CUBIC)
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used_classes_object = np.unique(pred_masks_object_detect[0])
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used_classes_lane = np.unique(pred_masks_lane_detect[0])
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fig_object = plt.figure()
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im = plt.imshow(tf.keras.preprocessing.image.array_to_img(pred_masks_object_detect[0]))
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patches_1 = [mpatches.Patch(color=im.cmap(im.norm(int(cls))), label="{}".format(label_map_object[int(cls)])) for cls in used_classes_object]
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# put those patched as legend-handles into the legend
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plt.legend(handles=patches_1, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.axis("off")
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fig_lane = plt.figure()
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im = plt.imshow(tf.keras.preprocessing.image.array_to_img(pred_masks_lane_detect[0]))
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patches_1 = [mpatches.Patch(color=im.cmap(im.norm(int(cls))), label="{}".format(lane_label_map[int(cls)])) for cls in used_classes_lane]
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# put those patched as legend-handles into the legend
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plt.legend(handles=patches_1, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
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plt.axis("off")
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return fig_lane
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webcam = gr.inputs.Image(shape=(800, 600), source="upload", type='file') #upload
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webapp = gr.interface.Interface(fn=segment_object, inputs=webcam, outputs="plot") #, live=False
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webapp.launch(debug=True)
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carla-image-segmentation-model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:b7f72d74610f5f8d39be28970b66a9aee8ccb8b50a768c57fa06803a62c4fbcc
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size 104177428
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lane-detection-for-carla-model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:9716b12103b745114ec39a54479eb2c38697925dfef06e88557354a08aa67bf1
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size 104173056
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requirements.txt
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tensorflow==2.8.0
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numpy==1.22.3
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opencv-python==4.5.5.62
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Pillow==9.0.1
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matplotlib==3.5.1
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pathlib==1.0.1
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