app file

app.py

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+import collections
+import os
+import tempfile
+from matplotlib import gridspec
+from matplotlib import pyplot as plt
+import numpy as np
+from PIL import Image
+import urllib
+import tensorflow as tf
+import gradio as gr
+from subprocess import call
+import sys
+import requests
+url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
+r = requests.get(url1, allow_redirects=True)
+open("city1.jpg", 'wb').write(r.content)
+url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
+r = requests.get(url2, allow_redirects=True)
+open("city2.jpg", 'wb').write(r.content)
+DatasetInfo = collections.namedtuple(
+    'DatasetInfo',
+    'num_classes, label_divisor, thing_list, colormap, class_names')
+def _cityscapes_label_colormap():
+  """Creates a label colormap used in CITYSCAPES segmentation benchmark.
+  See more about CITYSCAPES dataset at https://www.cityscapes-dataset.com/
+  M. Cordts, et al. "The Cityscapes Dataset for Semantic Urban Scene Understanding." CVPR. 2016.
+  Returns:
+    A 2-D numpy array with each row being mapped RGB color (in uint8 range).
+  """
+  colormap = np.zeros((256, 3), dtype=np.uint8)
+  colormap[0] = [128, 64, 128]
+  colormap[1] = [244, 35, 232]
+  colormap[2] = [70, 70, 70]
+  colormap[3] = [102, 102, 156]
+  colormap[4] = [190, 153, 153]
+  colormap[5] = [153, 153, 153]
+  colormap[6] = [250, 170, 30]
+  colormap[7] = [220, 220, 0]
+  colormap[8] = [107, 142, 35]
+  colormap[9] = [152, 251, 152]
+  colormap[10] = [70, 130, 180]
+  colormap[11] = [220, 20, 60]
+  colormap[12] = [255, 0, 0]
+  colormap[13] = [0, 0, 142]
+  colormap[14] = [0, 0, 70]
+  colormap[15] = [0, 60, 100]
+  colormap[16] = [0, 80, 100]
+  colormap[17] = [0, 0, 230]
+  colormap[18] = [119, 11, 32]
+  return colormap
+def _cityscapes_class_names():
+  return ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
+          'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
+          'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+          'bicycle')
+def cityscapes_dataset_information():
+  return DatasetInfo(
+      num_classes=19,
+      label_divisor=1000,
+      thing_list=tuple(range(11, 19)),
+      colormap=_cityscapes_label_colormap(),
+      class_names=_cityscapes_class_names())
+def perturb_color(color, noise, used_colors, max_trials=50, random_state=None):
+  """Pertrubs the color with some noise.
+  If `used_colors` is not None, we will return the color that has
+  not appeared before in it.
+  Args:
+    color: A numpy array with three elements [R, G, B].
+    noise: Integer, specifying the amount of perturbing noise (in uint8 range).
+    used_colors: A set, used to keep track of used colors.
+    max_trials: An integer, maximum trials to generate random color.
+    random_state: An optional np.random.RandomState. If passed, will be used to
+      generate random numbers.
+  Returns:
+    A perturbed color that has not appeared in used_colors.
+  """
+  if random_state is None:
+    random_state = np.random
+  for _ in range(max_trials):
+    random_color = color + random_state.randint(
+        low=-noise, high=noise + 1, size=3)
+    random_color = np.clip(random_color, 0, 255)
+    if tuple(random_color) not in used_colors:
+      used_colors.add(tuple(random_color))
+      return random_color
+  print('Max trial reached and duplicate color will be used. Please consider '
+        'increase noise in `perturb_color()`.')
+  return random_color
+def color_panoptic_map(panoptic_prediction, dataset_info, perturb_noise):
+  """Helper method to colorize output panoptic map.
+  Args:
+    panoptic_prediction: A 2D numpy array, panoptic prediction from deeplab
+      model.
+    dataset_info: A DatasetInfo object, dataset associated to the model.
+    perturb_noise: Integer, the amount of noise (in uint8 range) added to each
+      instance of the same semantic class.
+  Returns:
+    colored_panoptic_map: A 3D numpy array with last dimension of 3, colored
+      panoptic prediction map.
+    used_colors: A dictionary mapping semantic_ids to a set of colors used
+      in `colored_panoptic_map`.
+  """
+  if panoptic_prediction.ndim != 2:
+    raise ValueError('Expect 2-D panoptic prediction. Got {}'.format(
+        panoptic_prediction.shape))
+  semantic_map = panoptic_prediction // dataset_info.label_divisor
+  instance_map = panoptic_prediction % dataset_info.label_divisor
+  height, width = panoptic_prediction.shape
+  colored_panoptic_map = np.zeros((height, width, 3), dtype=np.uint8)
+  used_colors = collections.defaultdict(set)
+  # Use a fixed seed to reproduce the same visualization.
+  random_state = np.random.RandomState(0)
+  unique_semantic_ids = np.unique(semantic_map)
+  for semantic_id in unique_semantic_ids:
+    semantic_mask = semantic_map == semantic_id
+    if semantic_id in dataset_info.thing_list:
+      # For `thing` class, we will add a small amount of random noise to its
+      # correspondingly predefined semantic segmentation colormap.
+      unique_instance_ids = np.unique(instance_map[semantic_mask])
+      for instance_id in unique_instance_ids:
+        instance_mask = np.logical_and(semantic_mask,
+                                       instance_map == instance_id)
+        random_color = perturb_color(
+            dataset_info.colormap[semantic_id],
+            perturb_noise,
+            used_colors[semantic_id],
+            random_state=random_state)
+        colored_panoptic_map[instance_mask] = random_color
+    else:
+      # For `stuff` class, we use the defined semantic color.
+      colored_panoptic_map[semantic_mask] = dataset_info.colormap[semantic_id]
+      used_colors[semantic_id].add(tuple(dataset_info.colormap[semantic_id]))
+  return colored_panoptic_map, used_colors
+def vis_segmentation(image,
+                     panoptic_prediction,
+                     dataset_info,
+                     perturb_noise=60):
+  """Visualizes input image, segmentation map and overlay view."""
+  plt.figure(figsize=(30, 20))
+  grid_spec = gridspec.GridSpec(2, 2)
+  ax = plt.subplot(grid_spec[0])
+  plt.imshow(image)
+  plt.axis('off')
+  ax.set_title('input image', fontsize=20)
+  ax = plt.subplot(grid_spec[1])
+  panoptic_map, used_colors = color_panoptic_map(panoptic_prediction,
+                                                 dataset_info, perturb_noise)
+  plt.imshow(panoptic_map)
+  plt.axis('off')
+  ax.set_title('panoptic map', fontsize=20)
+  ax = plt.subplot(grid_spec[2])
+  plt.imshow(image)
+  plt.imshow(panoptic_map, alpha=0.7)
+  plt.axis('off')
+  ax.set_title('panoptic overlay', fontsize=20)
+  ax = plt.subplot(grid_spec[3])
+  max_num_instances = max(len(color) for color in used_colors.values())
+  # RGBA image as legend.
+  legend = np.zeros((len(used_colors), max_num_instances, 4), dtype=np.uint8)
+  class_names = []
+  for i, semantic_id in enumerate(sorted(used_colors)):
+    legend[i, :len(used_colors[semantic_id]), :3] = np.array(
+        list(used_colors[semantic_id]))
+    legend[i, :len(used_colors[semantic_id]), 3] = 255
+    if semantic_id < dataset_info.num_classes:
+      class_names.append(dataset_info.class_names[semantic_id])
+    else:
+      class_names.append('ignore')
+  plt.imshow(legend, interpolation='nearest')
+  ax.yaxis.tick_left()
+  plt.yticks(range(len(legend)), class_names, fontsize=15)
+  plt.xticks([], [])
+  ax.tick_params(width=0.0, grid_linewidth=0.0)
+  plt.grid('off')
+  return plt
+def run_cmd(command):
+    try:
+        print(command)
+        call(command, shell=True)
+    except KeyboardInterrupt:
+        print("Process interrupted")
+        sys.exit(1)
+MODEL_NAME = 'resnet50_os32_panoptic_deeplab_cityscapes_crowd_trainfine_saved_model'   
+_MODELS = ('resnet50_os32_panoptic_deeplab_cityscapes_crowd_trainfine_saved_model',
+           'resnet50_beta_os32_panoptic_deeplab_cityscapes_trainfine_saved_model',
+           'wide_resnet41_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
+           'swidernet_sac_1_1_1_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
+           'swidernet_sac_1_1_3_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
+           'swidernet_sac_1_1_4.5_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
+           'axial_swidernet_1_1_1_os16_axial_deeplab_cityscapes_trainfine_saved_model',
+           'axial_swidernet_1_1_3_os16_axial_deeplab_cityscapes_trainfine_saved_model',
+           'axial_swidernet_1_1_4.5_os16_axial_deeplab_cityscapes_trainfine_saved_model',
+           'max_deeplab_s_backbone_os16_axial_deeplab_cityscapes_trainfine_saved_model',
+           'max_deeplab_l_backbone_os16_axial_deeplab_cityscapes_trainfine_saved_model')
+_DOWNLOAD_URL_PATTERN = 'https://storage.googleapis.com/gresearch/tf-deeplab/saved_model/%s.tar.gz'
+_MODEL_NAME_TO_URL_AND_DATASET = {
+    model: (_DOWNLOAD_URL_PATTERN % model, cityscapes_dataset_information())
+    for model in _MODELS
+}
+MODEL_URL, DATASET_INFO = _MODEL_NAME_TO_URL_AND_DATASET[MODEL_NAME]
+model_dir = tempfile.mkdtemp()
+download_path = os.path.join(model_dir, MODEL_NAME + '.gz')
+urllib.request.urlretrieve(MODEL_URL, download_path)
+run_cmd("tar -xzvf " + download_path + " -C " + model_dir)
+LOADED_MODEL = tf.saved_model.load(os.path.join(model_dir, MODEL_NAME))
+def inference(image):
+    image = image.resize(size=(512, 512))
+    im = np.array(image)
+    output = LOADED_MODEL(tf.cast(im, tf.uint8))
+    return vis_segmentation(im, output['panoptic_pred'][0], DATASET_INFO)
+title = "Deeplab2"
+description = "demo for Deeplab2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2106.09748'>DeepLab2: A TensorFlow Library for Deep Labeling</a> | <a href='https://github.com/google-research/deeplab2'>Github Repo</a></p>"
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="plot", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+            ["city1.jpg"],
+            ["city2.jpg"]
+        ]).launch()