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import torch
import gradio as gr
from torchvision import transforms as T
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import imageio
classes=["Other","Anger","Contempt","Happiness","Surprise"]
# load a resnet18 model pretrained on ImageNet
# and turn off autograd on model's parameters
def load_model(idx):
model = torch.jit.load('/content/model_2_60acc.pt',map_location=torch.device('cpu')).eval()
for param in model.parameters():
param.requires_grad = False
return model
model=load_model(0)
# preprocess data
pretrained_std = torch.Tensor([0.229, 0.224, 0.225])
pretrained_mean = torch.Tensor([0.485, 0.456, 0.406])
optical_flow_t = T.Compose([
T.Resize((224,224)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225]),
])
ogpic=['/content/ogtest0.png','/content/ogtest1.png','/content/ogtest2.png','/content/ogtest3.png','/content/ogtest4.png','/content/ogtest5.png']
ofpic=['/content/oftest0.jpg','/content/oftest1.jpg','/content/oftest2.jpg','/content/oftest3.jpg','/content/oftest4.jpg','/content/oftest5.jpg']
diffpic=['/content/difftest0.png','/content/difftest1.png','/content/difftest2.png','/content/difftest3.png','/content/difftest4.png','/content/difftest5.png']
exp=[['/content/ogtest0.png'],['/content/ogtest1.png'],['/content/ogtest2.png'],['/content/ogtest3.png'],['/content/ogtest4.png'],['/content/ogtest5.png']]
vid=["/content/vidtest0.mp4","/content/vidtest1.mp4","/content/vidtest2.mp4","/content/vidtest3.mp4","/content/vidtest4.mp4","/content/vidtest5.mp4"]
actual=["Contempt","Other","Happiness","Anger","Other","Contempt"]
def main():
with gr.Blocks() as demo:
aa=gr.Variable(value=0)
def set_example_image(img):
aa.value=img
return gr.Image.update(value=exp[img][0])
def predss(img):
#print(Image.open(ofpic[a]).shape())
processed_img = optical_flow_t(Image.open(ofpic[aa.value]))
tb = torch.unsqueeze(processed_img, dim=0)
loaded_test = DataLoader(tb, batch_size=1,shuffle=False)
# get predictions
for i, inputs in enumerate(loaded_test):
with torch.no_grad():
output = model(inputs.to(torch.device('cpu'))) # Feed Network
probs = torch.nn.functional.softmax(output[0], dim=0)
top5_prob, top5_idx = torch.topk(probs, 5)
preds = {classes[idx]: prob.item() for idx, prob in zip(top5_idx, top5_prob)}
return ogpic[aa.value],ofpic[aa.value],diffpic[aa.value],vid[aa.value], preds,actual[aa.value]
gr.Markdown('''## Micro-expression recognition
''')
with gr.Box():
input_image = gr.Image(type="pil", label="Input Image")
example_images = gr.Dataset(components=[input_image],
samples=[['/content/ogtest0.png'],['/content/ogtest1.png'],['/content/ogtest2.png'],['/content/ogtest3.png'],['/content/ogtest4.png'],['/content/ogtest5.png']]
,type="index")
with gr.Row():
btn = gr.Button("Process")
gr.Markdown('''### Original Image''')
with gr.Box():
with gr.Row():
img_before = gr.Image(label="Original Image")
img_after1 = gr.Image(label="Different frame")
with gr.Row():
img_after = gr.Image(label="Optical flow")
label_predict = gr.Label(label="Prediction")
with gr.Box():
with gr.Row():
video = gr.Video(label="Original Video")
with gr.Row():
label_actual=gr.Label(label="Actual Emotion")
# events
btn.click(fn=predss,
inputs=[input_image],
outputs=[img_before,img_after,img_after1,video,label_predict,label_actual])
example_images.click(fn=set_example_image,
inputs=example_images,
outputs=example_images.components)
demo.launch()
if __name__ == '__main__':
main() |