Add application file

README.md

@@ -1,6 +1,6 @@
 ---
 title: Segformer Demo
-emoji: 💩
+emoji: 💻
 colorFrom: green
 colorTo: gray
 sdk: gradio

app.py

@@ -0,0 +1,125 @@
+import gradio as gr
+from matplotlib import gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+from torch import nn
+from PIL import Image
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
+
+feature_extractor = SegformerFeatureExtractor.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
+model = SegformerForSemanticSegmentation.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
+
+def sidewalk_palette():
+    """Sidewalk palette that maps each class to RGB values."""
+    return [
+        [0, 0, 0],
+        [216, 82, 24],
+        [255, 255, 0],
+        [125, 46, 141],
+        [118, 171, 47],
+        [161, 19, 46],
+        [255, 0, 0],
+        [0, 128, 128],
+        [190, 190, 0],
+        [0, 255, 0],
+        [0, 0, 255],
+        [170, 0, 255],
+        [84, 84, 0],
+        [84, 170, 0],
+        [84, 255, 0],
+        [170, 84, 0],
+        [170, 170, 0],
+        [170, 255, 0],
+        [255, 84, 0],
+        [255, 170, 0],
+        [255, 255, 0],
+        [33, 138, 200],
+        [0, 170, 127],
+        [0, 255, 127],
+        [84, 0, 127],
+        [84, 84, 127],
+        [84, 170, 127],
+        [84, 255, 127],
+        [170, 0, 127],
+        [170, 84, 127],
+        [170, 170, 127],
+        [170, 255, 127],
+        [255, 0, 127],
+        [255, 84, 127],
+        [255, 170, 127],
+    ]
+
+labels_list = []
+
+with open(r'labels.txt', 'r') as fp:
+    labels_list.extend(line[:-1] for line in fp)
+
+colormap = np.asarray(sidewalk_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 main(input_img):
+	input_img = Image.fromarray(input_img)
+
+	inputs = feature_extractor(images=input_img, return_tensors="pt")
+	outputs = model(**inputs)
+	logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
+
+	# First, rescale logits to original image size
+	upsampled_logits = nn.functional.interpolate(
+		logits,
+		size=input_img.size[::-1], # (height, width)
+		mode='bilinear',
+		align_corners=False
+	)
+
+	# Second, apply argmax on the class dimension
+	pred_seg = upsampled_logits.argmax(dim=1)[0]
+
+	color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8) # height, width, 3
+	palette = np.array(sidewalk_palette())
+	for label, color in enumerate(palette):
+		color_seg[pred_seg == label, :] = color
+
+	# Show image + mask
+	img = np.array(input_img) * 0.5 + color_seg * 0.5
+	pred_img = img.astype(np.uint8)    
+
+	return draw_plot(pred_img, pred_seg)
+
+demo = gr.Interface(main, 
+                    gr.Image(shape=(200, 200)), 
+                    outputs=['plot'], 
+					examples=["test.jpg"],
+                    allow_flagging='never')
+
+demo.launch()

labels.txt

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+unlabeled
+flat-road
+flat-sidewalk
+flat-crosswalk
+flat-cyclinglane
+flat-parkingdriveway
+flat-railtrack
+flat-curb
+human-person
+human-rider
+vehicle-car
+vehicle-truck
+vehicle-bus
+vehicle-tramtrain
+vehicle-motorcycle
+vehicle-bicycle
+vehicle-caravan
+vehicle-cartrailer
+construction-building
+construction-door
+construction-wall
+construction-fenceguardrail
+construction-bridge
+construction-tunnel
+construction-stairs
+object-pole
+object-trafficsign
+object-trafficlight
+nature-vegetation
+nature-terrain
+sky
+void-ground
+void-dynamic
+void-static
+void-unclear