Init

app.py

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+import os, os.path
+from os.path import splitext
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+import torch
+import torchvision
+import wget 
+
+
+destination_folder = "output"
+destination_for_weights = "weights"
+
+if os.path.exists(destination_for_weights):
+    print("The weights are at", destination_for_weights)
+else:
+    print("Creating folder at ", destination_for_weights, " to store weights")
+    os.mkdir(destination_for_weights)
+    
+segmentationWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/deeplabv3_resnet50_random.pt'
+
+if not os.path.exists(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL))):
+    print("Downloading Segmentation Weights, ", segmentationWeightsURL," to ",os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))
+    filename = wget.download(segmentationWeightsURL, out = destination_for_weights)
+else:
+    print("Segmentation Weights already present")
+
+torch.cuda.empty_cache()
+
+def collate_fn(x):
+    x, f = zip(*x)
+    i = list(map(lambda t: t.shape[1], x))
+    x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))
+    return x, f, i
+
+model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, aux_loss=False)
+model.classifier[-1] = torch.nn.Conv2d(model.classifier[-1].in_channels, 1, kernel_size=model.classifier[-1].kernel_size)
+
+print("loading weights from ", os.path.join(destination_for_weights, "deeplabv3_resnet50_random"))
+
+if torch.cuda.is_available():
+    print("cuda is available, original weights")
+    device = torch.device("cuda")
+    model = torch.nn.DataParallel(model)
+    model.to(device)
+    checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))
+    model.load_state_dict(checkpoint['state_dict'])
+else:
+    print("cuda is not available, cpu weights")
+    device = torch.device("cpu")
+    checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)), map_location = "cpu")
+    state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}
+    model.load_state_dict(state_dict_cpu)
+
+model.eval()
+
+def segment(inp):
+    x = inp.transpose([2, 0, 1])  #  channels-first
+    x = np.expand_dims(x, axis=0)  # adding a batch dimension    
+    
+    mean = x.mean(axis=(0, 2, 3))
+    std = x.std(axis=(0, 2, 3))
+    x = x - mean.reshape(1, 3, 1, 1)
+    x = x / std.reshape(1, 3, 1, 1)
+    
+    with torch.no_grad():
+        x = torch.from_numpy(x).type('torch.FloatTensor').to(device)
+        output = model(x)    
+    
+    y = output['out'].numpy()
+    y = y.squeeze()
+    
+    out = y>0    
+    
+    mask = inp.copy()
+    mask[out] = np.array([0, 0, 255])
+    
+    return mask
+
+import gradio as gr
+
+i = gr.inputs.Image(shape=(112, 112))
+o = gr.outputs.Image()
+
+examples = [["img1.jpg"], ["img2.jpg"]]
+title = None #"Left Ventricle Segmentation"
+description = "This semantic segmentation model identifies the left ventricle in echocardiogram images."
+# videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
+thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
+gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False,
+	title=title, description=description, thumbnail=thumbnail).launch()