Spaces:
Runtime error
Runtime error
File size: 4,086 Bytes
a06267d 010e04f a06267d b0fa478 a06267d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
# ! 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)
|