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…ents-update Vthonte requirements update
Added contributers
@all-contributors please add @abhijitjadhav1998 for founder of the project
…tjadhav1998-patch-1 Update README.md
…ntributors/add-abhijitjadhav1998 docs: add abhijitjadhav1998 as a contributor for projectManagement
…ntributors/add-vthonte docs: add vthonte as a contributor for maintenance
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The view file has a if condition where if the device is "gpu" use it else use "cpu", it should be "cuda" and not "gpu"
`from django.shortcuts import render, redirect
import torch
import torchvision
from torchvision import transforms, models
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import face_recognition
from torch.autograd import Variable
import time
import sys
from torch import nn
import json
import glob
import copy
from torchvision import models
import shutil
from PIL import Image as pImage
import time
from django.conf import settings
from .forms import VideoUploadForm
index_template_name = 'index.html'
predict_template_name = 'predict.html'
about_template_name = "about.html"
im_size = 112
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
sm = nn.Softmax()
inv_normalize = transforms.Normalize(mean=-1*np.divide(mean,std),std=np.divide([1,1,1],std))
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
train_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((im_size,im_size)),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
class Model(nn.Module):
class validation_dataset(Dataset):
def init(self,video_names,sequence_length=60,transform = None):
self.video_names = video_names
self.transform = transform
self.count = sequence_length
def im_convert(tensor, video_file_name):
""" Display a tensor as an image. """
image = tensor.to("cpu").clone().detach()
image = image.squeeze()
image = inv_normalize(image)
image = image.numpy()
image = image.transpose(1,2,0)
image = image.clip(0, 1)
# This image is not used
# cv2.imwrite(os.path.join(settings.PROJECT_DIR, 'uploaded_images', video_file_name+'_convert_2.png'),image*255)
return image
def im_plot(tensor):
image = tensor.cpu().numpy().transpose(1,2,0)
b,g,r = cv2.split(image)
image = cv2.merge((r,g,b))
image = image*[0.22803, 0.22145, 0.216989] + [0.43216, 0.394666, 0.37645]
image = image*255.0
plt.imshow(image.astype('uint8'))
plt.show()
def predict(model,img,path = './', video_file_name=""):
fmap,logits = model(img.to(device))
img = im_convert(img[:,-1,:,:,:], video_file_name)
params = list(model.parameters())
weight_softmax = model.linear1.weight.detach().cpu().numpy()
logits = sm(logits)
_,prediction = torch.max(logits,1)
confidence = logits[:,int(prediction.item())].item()*100
print('confidence of prediction:',logits[:,int(prediction.item())].item()*100)
return [int(prediction.item()),confidence]
def plot_heat_map(i, model, img, path = './', video_file_name=''):
fmap,logits = model(img.to(device))
params = list(model.parameters())
weight_softmax = model.linear1.weight.detach().cpu().numpy()
logits = sm(logits)
_,prediction = torch.max(logits,1)
idx = np.argmax(logits.detach().cpu().numpy())
bz, nc, h, w = fmap.shape
#out = np.dot(fmap[-1].detach().cpu().numpy().reshape((nc, hw)).T,weight_softmax[idx,:].T)
out = np.dot(fmap[i].detach().cpu().numpy().reshape((nc, hw)).T,weight_softmax[idx,:].T)
predict = out.reshape(h,w)
predict = predict - np.min(predict)
predict_img = predict / np.max(predict)
predict_img = np.uint8(255predict_img)
out = cv2.resize(predict_img, (im_size,im_size))
heatmap = cv2.applyColorMap(out, cv2.COLORMAP_JET)
img = im_convert(img[:,-1,:,:,:], video_file_name)
result = heatmap * 0.5 + img0.8*255
Saving heatmap - Start
heatmap_name = video_file_name+"heatmap"+str(i)+".png"
image_name = os.path.join(settings.PROJECT_DIR, 'uploaded_images', heatmap_name)
cv2.imwrite(image_name,result)
Saving heatmap - End
result1 = heatmap * 0.5/255 + img*0.8
r,g,b = cv2.split(result1)
result1 = cv2.merge((r,g,b))
return image_name
Model Selection
def get_accurate_model(sequence_length):
model_name = []
sequence_model = []
final_model = ""
list_models = glob.glob(os.path.join(settings.PROJECT_DIR, "models", "*.pt"))
ALLOWED_VIDEO_EXTENSIONS = set(['mp4','gif','webm','avi','3gp','wmv','flv','mkv'])
def allowed_video_file(filename):
#print("filename" ,filename.rsplit('.',1)[1].lower())
if (filename.rsplit('.',1)[1].lower() in ALLOWED_VIDEO_EXTENSIONS):
return True
else:
return False
def index(request):
if request.method == 'GET':
video_upload_form = VideoUploadForm()
if 'file_name' in request.session:
del request.session['file_name']
if 'preprocessed_images' in request.session:
del request.session['preprocessed_images']
if 'faces_cropped_images' in request.session:
del request.session['faces_cropped_images']
return render(request, index_template_name, {"form": video_upload_form})
else:
video_upload_form = VideoUploadForm(request.POST, request.FILES)
if video_upload_form.is_valid():
video_file = video_upload_form.cleaned_data['upload_video_file']
video_file_ext = video_file.name.split('.')[-1]
sequence_length = video_upload_form.cleaned_data['sequence_length']
video_content_type = video_file.content_type.split('/')[0]
if video_content_type in settings.CONTENT_TYPES:
if video_file.size > int(settings.MAX_UPLOAD_SIZE):
video_upload_form.add_error("upload_video_file", "Maximum file size 100 MB")
return render(request, index_template_name, {"form": video_upload_form})
def predict_page(request):
if request.method == "GET":
# Redirect to 'home' if 'file_name' is not in session
if 'file_name' not in request.session:
return redirect("ml_app:home")
if 'file_name' in request.session:
video_file = request.session['file_name']
if 'sequence_length' in request.session:
sequence_length = request.session['sequence_length']
path_to_videos = [video_file]
video_file_name = os.path.basename(video_file)
video_file_name_only = os.path.splitext(video_file_name)[0]
# Production environment adjustments
if not settings.DEBUG:
production_video_name = os.path.join('/home/app/staticfiles/', video_file_name.split('/')[3])
print("Production file name", production_video_name)
else:
production_video_name = video_file_name
def about(request):
return render(request, about_template_name)
def handler404(request,exception):
return render(request, '404.html', status=404)
def cuda_full(request):
return render(request, 'cuda_full.html')
`