Upload app.py

app.py

@@ -0,0 +1,112 @@
+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-cityscapes-1024-1024"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+ "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
+)
+
+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],
+ [87, 92, 204],
+ [120, 56, 123],
+ [45, 78, 123],
+ [156, 200, 56],
+ 
+ ]
+
+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=["city-1.jpg", "city-2.jpg", "city-3.jpg", "city-4.jpg", "city-5.jpg"],
+ allow_flagging='never')
+
+
+demo.launch()