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Runtime error
Runtime error
namkwonwoo
commited on
Commit
•
d5bb11a
1
Parent(s):
72c4c30
test
Browse files- app.py +110 -0
- labels.txt +18 -0
- person-1.jpg +0 -0
- person-2.jpg +0 -0
- person-3.jpg +0 -0
- person-4.jpg +0 -0
- person-5.jpg +0 -0
- requirements.txt +6 -0
app.py
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import gradio as gr
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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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feature_extractor = SegformerFeatureExtractor.from_pretrained(
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"mattmdjaga/segformer_b2_clothes"
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)
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model = TFSegformerForSemanticSegmentation.from_pretrained(
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"mattmdjaga/segformer_b2_clothes"
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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, 255, 255],
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[255, 0, 0],
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[0, 128, 0],
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[0, 0, 0],
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[128, 0, 128],
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[0, 0, 255],
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[0, 128, 0],
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[255, 182, 193],
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[128, 128, 128],
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[139, 69, 19],
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[139, 69, 19],
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[0, 255, 255],
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[0, 0, 128],
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[106, 90, 205],
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[64, 64, 64],
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[192, 192, 192],
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[240, 255, 240],
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[240, 255, 240]
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]
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labels_list = []
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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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colormap = np.asarray(ade_palette())
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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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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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def draw_plot(pred_img, seg):
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fig = plt.figure(figsize=(20, 15))
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grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
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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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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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def sepia(input_img):
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input_img = Image.fromarray(input_img)
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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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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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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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# 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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fig = draw_plot(pred_img, seg)
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return fig
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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=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
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allow_flagging='never')
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demo.launch()
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labels.txt
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Background
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Hat
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Hair
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Sunglasses
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Upper-clothes
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Skirt
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Pants
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Dress
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Belt
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Left-shoe
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Right-shoe
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Face
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Left-leg
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Right-leg
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Left-arm
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Right-arm
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Bag
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Scarf
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person-1.jpg
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person-2.jpg
ADDED
person-3.jpg
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person-4.jpg
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person-5.jpg
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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1 |
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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
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