Upload app.py

app.py

@@ -0,0 +1,242 @@
+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(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [
+        [204, 87, 92],
+        [112, 185, 212],
+        [45, 189, 106],
+        [234, 123, 67],
+        [78, 56, 123],
+        [210, 32, 89],
+        [90, 180, 56],
+        [155, 102, 200],
+        [33, 147, 176],
+        [255, 183, 76],
+        [67, 123, 89],
+        [190, 60, 45],
+        [134, 112, 200],
+        [56, 45, 189],
+        [200, 56, 123],
+        [230, 127, 34],
+        [179, 51, 126],
+        [122, 122, 201],
+        [255, 221, 101],
+        [97, 48, 88],
+        [225, 49, 112],
+        [55, 120, 254],
+        [181, 43, 25],
+        [212, 59, 3],
+        [51, 0, 0],
+        [0, 51, 0],
+        [0, 0, 51],
+        [153, 153, 153],
+        [255, 127, 0],
+        [128, 255, 0],
+        [0, 128, 255],
+        [255, 0, 128],
+        [128, 255, 128],
+        [255, 0, 0],
+        [128, 255, 0],
+        [255, 0, 128],
+        [0, 128, 0],
+        [0, 0, 128],
+        [0, 128, 255],
+        [128, 0, 255],
+        [255, 0, 128],
+        [128, 255, 128],
+        [255, 0, 0],
+        [0, 128, 255],
+        [128, 0, 255],
+        [0, 0, 0],
+        [255, 128, 0],
+        [0, 255, 0],
+        [0, 0, 128],
+        [0, 0, 0],
+        [255, 0, 0],
+        [128, 0, 255],
+        [0, 128, 0],
+        [255, 255, 128],
+        [255, 0, 255],
+        [255, 255, 0],
+        [128, 0, 0],
+        [255, 128, 128],
+        [0, 128, 255],
+        [128, 0, 255],
+        [0, 0, 255],
+        [0, 255, 255],
+        [255, 255, 0],
+        [255, 0, 255],
+        [255, 128, 0],
+        [255, 255, 255],
+        [128, 0, 0],
+        [255, 0, 255],
+        [255, 255, 0],
+        [0, 0, 128],
+        [255, 255, 255],
+        [0, 255, 0],
+        [0, 0, 0],
+        [255, 128, 0],
+        [0, 255, 128],
+        [255, 0, 0],
+        [0, 0, 255],
+        [128, 255, 0],
+        [255, 255, 128],
+        [255, 255, 0],
+        [255, 128, 128],
+        [255, 0, 128],
+        [255, 128, 255],
+        [255, 0, 128],
+        [255, 255, 0],
+        [255, 128, 0],
+        [204, 87, 92],
+        [128, 255, 0],
+        [255, 0, 255],
+        [0, 255, 128], 
+        [90, 180, 56],
+        [91, 1, 5],
+        [92, 64, 34],
+        [93, 128, 0],
+        [94, 255, 0],
+        [95, 34, 87],
+        [96, 86, 145],
+        [97, 123, 98],
+        [98, 0, 255],
+        [99, 255, 128],
+        [100, 45, 122],
+        [101, 134, 245],
+        [102, 32, 23],
+        [103, 56, 0],
+        [104, 76, 98],
+        [105, 176, 90], 
+        [106, 102, 200],
+        [107, 56, 78],
+        [108, 23, 89],
+        [109, 45, 200],
+        [110, 87, 5],
+        [111, 200, 67],
+        [112, 34, 23],
+        [113, 98, 76],
+        [114, 122, 56],
+        [115, 56, 23],
+        [116, 78, 90],
+        [117, 200, 45],
+        [118, 23, 56],
+        [119, 56, 189],
+        [120, 0, 45],
+        [121, 0, 0],
+        [122, 89, 34],
+        [123, 200, 1],
+        [124, 32, 45],
+        [125, 89, 0],
+        [126, 0, 200],
+        [127, 90, 200],
+        [128, 45, 200],
+        [129, 0, 123],
+        [130, 200, 23],
+        [131, 32, 200],
+        [132, 56, 23],
+        [133, 87, 98],
+        [134, 0, 32],
+        [135, 90, 0],
+        [136, 45, 23],
+        [137, 0, 89],
+        [138, 200, 0],
+        [139, 45, 23],
+        [140, 123, 0],
+        [141, 45, 200],
+        [142, 98, 23],
+        [143, 0, 98],
+        [144, 200, 45],
+        [145, 0, 23],
+        [146, 23, 87],
+        [147, 45, 0],
+        [148, 0, 89],
+        [149, 200, 32]
+    ]
+
+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=["test1.jpg", "test2.jpg", "test3.jpg", "test4.jpg"],
+                    allow_flagging='never')
+
+
+demo.launch()