test

app.py

@@ -0,0 +1,118 @@
+import gradio as gr
+import numpy as np
+from huggingface_hub import hf_hub_url, cached_download
+import PIL
+import onnx
+import onnxruntime
+
+config_file_url = hf_hub_url("Jacopo/ToonClip", filename="model.onnx")
+model_file = cached_download(config_file_url)
+
+onnx_model = onnx.load(model_file)
+onnx.checker.check_model(onnx_model)
+
+opts = onnxruntime.SessionOptions()
+opts.intra_op_num_threads = 16
+ort_session = onnxruntime.InferenceSession(model_file, sess_options=opts)
+
+input_name = ort_session.get_inputs()[0].name
+output_name = ort_session.get_outputs()[0].name
+
+def normalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)):
+    # x = (x - mean) / std
+    x = np.asarray(x, dtype=np.float32)
+    if len(x.shape) == 4:
+        for dim in range(3):
+            x[:, dim, :, :] = (x[:, dim, :, :] - mean[dim]) / std[dim]
+    if len(x.shape) == 3:
+        for dim in range(3):
+            x[dim, :, :] = (x[dim, :, :] - mean[dim]) / std[dim]
+
+    return x 
+
+def denormalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)):
+    # x = (x * std) + mean
+    x = np.asarray(x, dtype=np.float32)
+    if len(x.shape) == 4:
+        for dim in range(3):
+            x[:, dim, :, :] = (x[:, dim, :, :] * std[dim]) + mean[dim]
+    if len(x.shape) == 3:
+        for dim in range(3):
+            x[dim, :, :] = (x[dim, :, :] * std[dim]) + mean[dim]
+
+    return x 
+
+def nogan(input_img):
+    i = np.asarray(input_img)
+    i = i.astype("float32")
+    i = np.transpose(i, (2, 0, 1))
+    i = np.expand_dims(i, 0)
+    i = i / 255.0
+    i = normalize(i, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+
+    ort_outs = ort_session.run([output_name], {input_name: i})
+    output = ort_outs
+    output = output[0][0]
+
+    output = denormalize(output, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+    output = output * 255.0
+    output = output.astype('uint8')
+    output = np.transpose(output, (1, 2, 0))
+    output_image = PIL.Image.fromarray(output, 'RGB')
+
+    return output_image
+
+title = "ToonClip Comics Hero Demo"
+description = """
+Gradio demo for ToonClip, a UNet++ network with MobileNet v3 backbone optimized for mobile frameworks and trained with VGG Perceptual Feature Loss trained with PyTorch Lighting.
+To use it, simply upload an image with a face or choose an example from the list below.
+"""
+article = """
+<style> 
+	.boxes{ 
+	    width:50%; 
+	    float:left; 
+    } 
+	#mainDiv{ 
+		width:50%; 
+        margin:auto; 
+	} 
+	img{ 
+		max-width:100%; 
+	}
+</style> 
+<p style='text-align: center'>The \"ToonClip\" model was trained by <a href='https://twitter.com/JacopoMangia' target='_blank'>Jacopo Mangiavacchi</a> and available at <a href='https://github.com/jacopomangiavacchi/ComicsHeroMobileUNet' target='_blank'>Github Repo ComicsHeroMobileUNet</a></p>
+<p style='text-align: center'>The \"Comics Hero dataset\" used to train this model was produced by <a href='https://linktr.ee/Norod78' target='_blank'>Doron Adler</a> and available at <a href='https://github.com/Norod/U-2-Net-StyleTransfer' target='_blank'>Github Repo Comics hero U2Net</a></p>
+<p style='text-align: center'>The \"ToonClip\" iOS mobile app using a CoreML version of this model is available on Apple App Store at <a href='https://apps.apple.com/us/app/toonclip/id1536285338' target='_blank'>ToonClip</a></p>
+<p style='text-align: center'>samples: </p>
+<p>
+  <div id='mainDiv'> 
+    <div id='divOne' class='boxes'> 
+	<img src='https://hf.space/gradioiframe/Jacopo/ComicsHeroMobileUNet/file/Example01.jpeg' alt='Example01'/>
+    </div> 
+    <div id='divTwo' class='boxes'> 
+	<img <img src='https://hf.space/gradioiframe/Jacopo/ComicsHeroMobileUNet/file/Output01.png' alt='Output01'/>
+    </div> 
+    <div id='divOne' class='boxes'> 
+	<img src='https://hf.space/gradioiframe/Jacopo/ComicsHeroMobileUNet/file/Example01.jpeg' alt='Example01'/>
+    </div> 
+    <div id='divTwo' class='boxes'> 
+	<img <img src='https://hf.space/gradioiframe/Jacopo/ComicsHeroMobileUNet/file/Output01.png' alt='Output01'/> 
+    </div> 
+  </div>
+</p>
+"""
+examples=[['Example01.jpeg']]
+
+iface = gr.Interface(
+    nogan, 
+    gr.inputs.Image(type="pil", shape=(1024, 1024)),
+    gr.outputs.Image(type="pil"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    enable_queue=True,
+    live=True)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,4 @@
+pillow==9.0.0
+numpy
+onnx
+onnxruntime