Delete Hugging_Face

Hugging_Face/.gitattributes

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Hugging_Face/README.md

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----
-title: Hugging Face
-emoji: 👀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 4.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Hugging_Face/app.py

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-import gradio as gr
-
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
-
-feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "mattmdjaga/segformer_b2_clothes"
-)
-model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "mattmdjaga/segformer_b2_clothes"
-)
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [255,0,0],
-        [255,255,0],
-        [255,94,0],
-        [1,0,255],
-        [255,0,221],
-        [255,0,0],
-        [255,0,221],
-        [0,130,153],
-        [1,0,255],
-        [53,53,53],
-        [255,0,0],
-        [250,236,197],
-        [171,242,0],
-        [255,255,0],
-        [255,94,8],
-        [255,255,255],
-        [234,234,234],
-        [255,255,255]
-    ]
-
-labels_list = []
-
-with open(r'labels.txt', 'r') as fp:
-    for line in fp:
-        labels_list.append(line[:-1])
-
-colormap = np.asarray(ade_palette())
-
-def label_to_color_image(label):
-    if label.ndim != 2:
-        raise ValueError("Expect 2-D input label")
-
-    if np.max(label) >= len(colormap):
-        raise ValueError("label value too large.")
-    return colormap[label]
-
-def draw_plot(pred_img, seg):
-    fig = plt.figure(figsize=(20, 15))
-
-    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
-
-    plt.subplot(grid_spec[0])
-    plt.imshow(pred_img)
-    plt.axis('off')
-    LABEL_NAMES = np.asarray(labels_list)
-    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
-    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
-
-    unique_labels = np.unique(seg.numpy().astype("uint8"))
-    ax = plt.subplot(grid_spec[1])
-    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
-    ax.yaxis.tick_right()
-    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
-    plt.xticks([], [])
-    ax.tick_params(width=0.0, labelsize=25)
-    return fig
-
-def sepia(input_img):
-    input_img = Image.fromarray(input_img)
-
-    inputs = feature_extractor(images=input_img, return_tensors="tf")
-    outputs = model(**inputs)
-    logits = outputs.logits
-
-    logits = tf.transpose(logits, [0, 2, 3, 1])
-    logits = tf.image.resize(
-        logits, input_img.size[::-1]
-    )  # We reverse the shape of `image` because `image.size` returns width and height.
-    seg = tf.math.argmax(logits, axis=-1)[0]
-
-    color_seg = np.zeros(
-        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
-    )  # height, width, 3
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
-
-    # Show image + mask
-    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
-    pred_img = pred_img.astype(np.uint8)
-
-    fig = draw_plot(pred_img, seg)
-    return fig
-
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(400, 600)),
-                    outputs=['plot'],
-                    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg","person-5.jpg"],
-                    allow_flagging='never')
-
-
-demo.launch()

Hugging_Face/labels.txt

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-Background
-Hat
-Hair
-Sunglasses
-Upper-clothes
-Skirt
-Pants
-Dress
-Belt
-Left-shoe
-Right-shoe
-Face
-Left-leg
-Right-leg
-Left-arm
-Right-arm
-Bag
-Scarf

Hugging_Face/requirements.txt

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-torch
-transformers
-tensorflow
-numpy
-Image
-matplotlib