Upload app.py

app.py

@@ -0,0 +1,94 @@
+import cv2
+import torch
+import numpy as np
+import gradio as gr
+from PIL import Image
+from super_image import ImageLoader, EdsrModel, MsrnModel, MdsrModel, AwsrnModel, A2nModel, CarnModel, PanModel, \
+    HanModel, DrlnModel, RcanModel
+
+title = "super-image"
+description = "State of the Art Image Super-Resolution Models."
+article = "<p style='text-align: center'><a href='https://github.com/eugenesiow/super-image'>Github Repo</a>" \
+          "| <a href='https://eugenesiow.github.io/super-image/'>Documentation</a> " \
+          "| <a href='https://github.com/eugenesiow/super-image#scale-x2'>Models</a></p>"
+
+
+def get_model(model_name, scale):
+    if model_name == 'EDSR':
+        model = EdsrModel.from_pretrained('eugenesiow/edsr', scale=scale)
+    elif model_name == 'MSRN':
+        model = MsrnModel.from_pretrained('eugenesiow/msrn', scale=scale)
+    elif model_name == 'MDSR':
+        model = MdsrModel.from_pretrained('eugenesiow/mdsr', scale=scale)
+    elif model_name == 'AWSRN-BAM':
+        model = AwsrnModel.from_pretrained('eugenesiow/awsrn-bam', scale=scale)
+    elif model_name == 'A2N':
+        model = A2nModel.from_pretrained('eugenesiow/a2n', scale=scale)
+    elif model_name == 'CARN':
+        model = CarnModel.from_pretrained('eugenesiow/carn', scale=scale)
+    elif model_name == 'PAN':
+        model = PanModel.from_pretrained('eugenesiow/pan', scale=scale)
+    elif model_name == 'HAN':
+        model = HanModel.from_pretrained('eugenesiow/han', scale=scale)
+    elif model_name == 'DRLN':
+        model = DrlnModel.from_pretrained('eugenesiow/drln', scale=scale)
+    elif model_name == 'RCAN':
+        model = RcanModel.from_pretrained('eugenesiow/rcan', scale=scale)
+    else:
+        model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=scale)
+    return model
+
+
+def inference(img, scale_str, model_name):
+    max_res = 1024
+    scale = int(scale_str.replace('x', ''))
+    width, height = img.size
+    print(width, height)
+    if width > max_res or height > max_res:
+        img = img.thumbnail((max_res, max_res), Image.ANTIALIAS)
+    model = get_model(model_name, scale)
+    try:
+        inputs = ImageLoader.load_image(img)
+        preds = model(inputs)
+        preds = preds.data.cpu().numpy()
+        pred = preds[0].transpose((1, 2, 0)) * 255.0
+        return Image.fromarray(pred.astype('uint8'), 'RGB')
+    except Exception as e:
+        print(e)
+        return None
+
+
+torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/baby_mini_d3_gaussian.bmp',
+                               'baby.bmp')
+torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/woman_mini_d3_gaussian.bmp',
+                               'woman.bmp')
+torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/bird_mini_d4_gaussian.bmp',
+                               'bird.bmp')
+
+# models = ['EDSR-base', 'DRLN', 'EDSR', 'MDSR', 'A2N', 'PAN', 'AWSRN-BAM', 'MSRN']
+models = ['EDSR-base', 'A2N', 'PAN', 'AWSRN-BAM', 'MSRN']
+scales = [2, 3, 4]
+for model_name in models:
+    for scale in scales:
+        get_model(model_name, scale)
+
+gr.Interface(
+    inference,
+    [
+        gr.inputs.Image(type="pil", label="Input"),
+        gr.inputs.Radio(["x2", "x3", "x4"], label='scale'),
+        gr.inputs.Dropdown(choices=models,
+                           label='Model')
+    ],
+    gr.outputs.Image(type="pil", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+        ['baby.bmp', 'x2', 'EDSR-base'],
+        ['woman.bmp', 'x3', 'MSRN'],
+        ['bird.bmp', 'x4', 'PAN']
+    ],
+    enable_queue=True,
+    allow_flagging=False,
+    ).launch(debug=False)