김영중 commited on
Commit
cdfee38
1 Parent(s): 5dd0233

modify segmentation

Browse files
Files changed (8) hide show
  1. app.py +110 -0
  2. labels.txt +18 -0
  3. person-1.jpg +0 -0
  4. person-2.jpg +0 -0
  5. person-3.jpg +0 -0
  6. person-4.jpg +0 -0
  7. person-5.jpg +0 -0
  8. requirements.txt +6 -0
app.py ADDED
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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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+ "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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+
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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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+ [166,166,166],
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+ [255,0,0],
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+ [0,216,255],
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+ [134,229,127],
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+ [165,102,255],
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+ [229,216,92],
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+ [250,237,125],
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+ [196,183,59],
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+ [0,0,0],
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+ [0,84,255],
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+ [1,0,255],
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+ [255,255,255],
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+ [242,150,97],
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+ [204,114,61],
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+ [71,200,62],
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+ [47,157,39],
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+ [128,65,217],
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+ [255,0,221],
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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=["person-1.jpeg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.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
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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
person-1.jpg ADDED
person-2.jpg ADDED
person-3.jpg ADDED
person-4.jpg ADDED
person-5.jpg ADDED
requirements.txt ADDED
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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