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91d5ff0
1 Parent(s): d3b8fe1

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Files changed (8) hide show
  1. .gitattributes +1 -0
  2. app.py +127 -0
  3. labels.txt +35 -0
  4. requirements.txt +6 -0
  5. sidewalk-1.jpg +0 -0
  6. sidewalk-2.jpg +3 -0
  7. sidewalk-3.jpg +0 -0
  8. sidewalk-4.jpg +0 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ sidewalk-2.jpg filter=lfs diff=lfs merge=lfs -text
app.py ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import gradio as gr
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+
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+ from matplotlib import gridspec
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+ import matplotlib.pyplot as plt
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+ import numpy as np
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+ from PIL import Image
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+ import tensorflow as tf
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+ from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
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+
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+ feature_extractor = SegformerFeatureExtractor.from_pretrained(
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+ "nickmuchi/segformer-b4-finetuned-segments-sidewalk"
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+ )
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+ model = TFSegformerForSemanticSegmentation.from_pretrained(
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+ "nickmuchi/segformer-b4-finetuned-segments-sidewalk"
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+ )
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+
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+ def ade_palette():
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+ """ADE20K palette that maps each class to RGB values."""
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+ return [
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+ [255, 0, 0],
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+ [255, 187, 0],
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+ [171, 242, 0],
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+ [29, 219, 22],
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+ [0, 216, 255],
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+ [0, 84, 255],
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+ [95, 0, 255],
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+ [255, 0, 221],
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+ [33, 147, 176],
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+ [255, 183, 76],
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+ [67, 123, 89],
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+ [190, 60, 45],
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+ [134, 112, 200],
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+ [56, 45, 189],
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+ [92, 107, 168],
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+ [17, 118, 176],
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+ [59, 50, 174],
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+ [206, 40, 143],
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+ [44, 19, 142],
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+ [23, 168, 75],
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+ [54, 57, 189],
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+ [144, 21, 15],
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+ [15, 176, 35],
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+ [107, 19, 79],
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+ [204, 52, 114],
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+ [48, 173, 83],
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+ [11, 120, 53],
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+ [206, 104, 28],
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+ [20, 31, 153],
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+ [27, 21, 93],
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+ [11, 206, 138],
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+ [112, 30, 83],
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+ [68, 91, 152],
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+ [153, 13, 43],
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+ [25, 114, 54],
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+ ]
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+
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+ labels_list = []
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+
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+ with open(r'labels.txt', 'r') as fp:
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+ for line in fp:
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+ labels_list.append(line[:-1])
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+
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+ colormap = np.asarray(ade_palette())
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+
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+ def label_to_color_image(label):
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+ if label.ndim != 2:
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+ raise ValueError("Expect 2-D input label")
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+
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+ if np.max(label) >= len(colormap):
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+ raise ValueError("label value too large.")
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+ return colormap[label]
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+
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+ def draw_plot(pred_img, seg):
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+ fig = plt.figure(figsize=(20, 15))
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+
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+ grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
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+
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+ plt.subplot(grid_spec[0])
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+ plt.imshow(pred_img)
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+ plt.axis('off')
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+ LABEL_NAMES = np.asarray(labels_list)
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+ FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
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+ FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
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+
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+ unique_labels = np.unique(seg.numpy().astype("uint8"))
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+ ax = plt.subplot(grid_spec[1])
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+ plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
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+ ax.yaxis.tick_right()
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+ plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
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+ plt.xticks([], [])
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+ ax.tick_params(width=0.0, labelsize=25)
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+ return fig
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+
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+ def sepia(input_img):
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+ input_img = Image.fromarray(input_img)
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+
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+ inputs = feature_extractor(images=input_img, return_tensors="tf")
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+ outputs = model(**inputs)
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+ logits = outputs.logits
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+
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+ logits = tf.transpose(logits, [0, 2, 3, 1])
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+ logits = tf.image.resize(
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+ logits, input_img.size[::-1]
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+ ) # We reverse the shape of `image` because `image.size` returns width and height.
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+ seg = tf.math.argmax(logits, axis=-1)[0]
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+
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+ color_seg = np.zeros(
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+ (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
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+ ) # height, width, 3
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+ for label, color in enumerate(colormap):
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+ color_seg[seg.numpy() == label, :] = color
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+
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+ # Show image + mask
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+ pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
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+ pred_img = pred_img.astype(np.uint8)
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+
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+ fig = draw_plot(pred_img, seg)
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+ return fig
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+
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+ demo = gr.Interface(fn=sepia,
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+ inputs=gr.Image(shape=(400, 600)),
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+ outputs=['plot'],
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+ examples=["sidewalk-1.jpg", "sidewalk-2.jpg", "sidewalk-3.jpg", "sidewalk-4.jpg"],
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+ allow_flagging='never')
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+
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+
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+ demo.launch()
labels.txt ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ unlabeled
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+ flat-road
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+ flat-sidewalk
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+ flat-crosswalk
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+ flat-cyclinglane
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+ flat-parkingdriveway
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+ flat-railtrack
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+ flat-curb
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+ human-person
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+ human-rider
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+ vehicle-car
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+ vehicle-truck
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+ vehicle-bus
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+ vehicle-tramtrain
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+ vehicle-motorcycle
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+ vehicle-bicycle
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+ vehicle-caravan
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+ vehicle-cartrailer
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+ construction-building
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+ construction-door
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+ construction-wall
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+ construction-fenceguardrail
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+ construction-bridge
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+ construction-tunnel
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+ construction-stairs
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+ object-pole
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+ object-trafficsign
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+ object-trafficlight
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+ nature-vegetation
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+ nature-terrain
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+ sky
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+ void-ground
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+ void-dynamic
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+ void-static
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+ void-unclear
requirements.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
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+ torch
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+ transformers
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+ tensorflow
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+ numpy
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+ Image
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+ matplotlib
sidewalk-1.jpg ADDED
sidewalk-2.jpg ADDED

Git LFS Details

  • SHA256: 28699f0852f6e778ef55c05a42c6c57f54d2fb98c2d7489d67881abf087a98de
  • Pointer size: 132 Bytes
  • Size of remote file: 3.39 MB
sidewalk-3.jpg ADDED
sidewalk-4.jpg ADDED