initial

app.py

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+import paddlehub as hub
+import gradio as gr
+import requests
+import numpy as np
+import matplotlib.pyplot as plt
+
+model = hub.Module(name='deeplabv3p_resnet50_cityscapes')
+
+url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
+r = requests.get(url1, allow_redirects=True)
+open("city1.jpg", 'wb').write(r.content)
+url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
+r = requests.get(url2, allow_redirects=True)
+open("city2.jpg", 'wb').write(r.content)
+
+
+colormap = np.zeros((256, 3), dtype=np.uint8)
+colormap[0] = [128, 64, 128]
+colormap[1] = [244, 35, 232]
+colormap[2] = [70, 70, 70]
+colormap[3] = [102, 102, 156]
+colormap[4] = [190, 153, 153]
+colormap[5] = [153, 153, 153]
+colormap[6] = [250, 170, 30]
+colormap[7] = [220, 220, 0]
+colormap[8] = [107, 142, 35]
+colormap[9] = [152, 251, 152]
+colormap[10] = [70, 130, 180]
+colormap[11] = [220, 20, 60]
+colormap[12] = [255, 0, 0]
+colormap[13] = [0, 0, 142]
+colormap[14] = [0, 0, 70]
+colormap[15] = [0, 60, 100]
+colormap[16] = [0, 80, 100]
+colormap[17] = [0, 0, 230]
+colormap[18] = [119, 11, 32]
+
+def applyColormap(img,colormap):
+    ret = np.zeros((img.shape[0],img.shape[1],3), dtype=np.uint8)
+    for y in range(img.shape[0]):
+        for x in range(img.shape[1]):
+            ret[y,x] = colormap[int(img[y,x])]
+    return ret.astype(np.uint8)
+
+
+def inference(image):
+    img = np.array(image)[:,:,::-1]
+    result = model.predict(images=[img], visualization=True)[0]
+    result_color = applyColormap(result,colormap)
+    return result_color
+
+title = "PaddleHub: DeepLabv3p R50 Cityscapes"
+description = "demo for PaddleHub. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\nModel: deeplabv3p_resnet50_cityscapes"
+article = "<p style='text-align: center'><a href='https://www.paddlepaddle.org.cn/hubdetail?name=deeplabv3p_resnet50_cityscapes&en_category=ImageSegmentation'>PaddleHub page</a></p>"
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="numpy", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+            ["city1.jpg"],
+            ["city2.jpg"]
+        ]).launch()

requirements.txt

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+paddlehub
+