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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, css=css, allow_flagging=False, analytics_enabled=False,
title=title, description=description, thumbnail=thumbnail).launch()
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