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video_test.py
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video_test.py
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import os
#change this to your working directory
current_dir = r'~/~/Documents/graduation_project/codes/real-time-action-recognition/untrimmed_work'
#os.chdir(current_dir + 'real-time-action-recognition')
import time
import torch
import torch.nn.parallel
import torchvision
import cv2
import torchvision.transforms as Transforms
from numpy.random import randint
import operator
#from matplotlib import pyplot as plt
from Modified_CNN import TSN_model
from transforms import *
import argparse
parser = argparse.ArgumentParser(
description="Standard video-level testing")
parser.add_argument('dataset', type=str, choices=['ucf101', 'hmdb51', 'kinetics'])
parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff'])
parser.add_argument('weights', type=str)
parser.add_argument('--arch', type=str, default="BNInception")
parser.add_argument('--test_segments', type=int, default=25)
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type', type=str, default='avg',
choices=['avg', 'max', 'topk'])
parser.add_argument('--k', type=int, default=3)
parser.add_argument('--dropout', type=float, default=0.7)
parser.add_argument('--classInd_file', type=str, default='')
parser.add_argument('--video', type=str, default='')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
args = parser.parse_args()
#this function returns a dictionary (keys are string label numbers & values are action labels)
def label_dic(classInd):
action_label={}
with open(classInd) as f:
content = f.readlines()
content = [x.strip('\r\n') for x in content]
f.close()
for line in content:
label, action = line.split(' ')
if action not in action_label.keys():
action_label[label] = action
return action_label
#this function takes one video at a time and outputs the first 5 scores
def one_video():
num_crop = args.test_crops
test_segments = args.test_segments
#this function do forward propagation and returns scores
def eval_video(data):
"""
Evaluate single video
video_data : Tuple has 3 elments (data in shape (crop_number,num_segments*length,H,W), label)
return : predictions and labels
"""
if args.modality == 'RGB':
length = 3
elif args.modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality " + args.modality)
with torch.no_grad():
#reshape data to be in shape of (num_segments*crop_number,length,H,W)
input = data.view(-1, length, data.size(1), data.size(2))
#Forword Propagation
output = model(input)
output_np = output.data.cpu().numpy().copy()
#Reshape numpy array to (num_crop,num_segments,num_classes)
output_np = output_np.reshape((num_crop, test_segments, num_class))
#Take mean of cropped images to be in shape (num_segments,1,num_classes)
output_np = output_np.mean(axis=0).reshape((test_segments,1,num_class))
output_np = output_np.mean(axis=0)
return output_np
action_label = label_dic(args.classInd_file)
if args.dataset == 'ucf101':
num_class = 101
else:
raise ValueError('Unkown dataset: ' + args.dataset)
model = TSN_model(num_class, 1, args.modality,
base_model_name=args.arch, consensus_type='avg', dropout=args.dropout)
#load the weights of your model training
checkpoint = torch.load(args.weights)
print("epoch {}, best acc1@: {}" .format(checkpoint['epoch'], checkpoint['best_acc1']))
base_dict = {'.'.join(k.split('.')[1:]): v for k,v in list(checkpoint['state_dict'].items())}
model.load_state_dict(base_dict)
#test_crops is set to 1 for fast video evaluation
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(model.scale_size),
GroupCenterCrop(model.input_size),
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(model.input_size, model.scale_size)
])
else:
raise ValueError("Only 1 and 10 crops are supported while we got {}".format(test_crops))
#Required transformations
transform = torchvision.transforms.Compose([
cropping,
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
GroupNormalize(model.input_mean, model.input_std),
])
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
else:
devices = list(range(args.workers))
model = torch.nn.DataParallel(model.cuda(devices[0]), device_ids=devices)
model.eval()
softmax = torch.nn.Softmax()
scores = torch.tensor(np.zeros((1,101)), dtype=torch.float32).cuda()
frames = []
capture = cv2.VideoCapture(args.video)
frame_count = 0
while True:
ret, orig_frame = capture.read()
if ret is True:
frame = cv2.cvtColor(orig_frame, cv2.COLOR_BGR2RGB)
else:
break
#RGB_frame is used for plotting a frame with text of top-5 scores on it
RGB_frame = frame
#use .fromarray function to be able to apply different data augmentations
frame = Image.fromarray(frame)
frame_count += 1
frames.append(frame)
print(frame_count)
# When everything done, release the capture
capture.release()
#cv2.destroyAllWindows()
#to evaluate the processing time
start_time = time.time()
'''
images = [cv2.imread(file) for file in glob.glob(args.video + "/*.jpg")]
for frame in images:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame)
frames.append(frame)
'''
#this function used to pick 25 frames only from the whole video
def frames_indices(frames):
FPSeg = len(frames) // test_segments
offset = [x*FPSeg for x in range(test_segments)]
random_indices = list(randint(FPSeg,size=test_segments))
frame_indices = [sum(i) for i in zip(random_indices,offset)]
return frame_indices
indices = frames_indices(frames)
frames_a = operator.itemgetter(*indices)(frames)
frames_a = transform(frames_a).cuda()
scores = eval_video(frames_a)
scores = softmax(torch.FloatTensor(scores))
scores = scores.data.cpu().numpy().copy()
print("scores of the segments.")
print(scores)
print('---------------------------------')
print("scores size", scores.size)
end_time = time.time() - start_time
print("time taken: ", end_time)
# Display the resulting frame and the classified action
font = cv2.FONT_HERSHEY_SIMPLEX
y0, dy = 300, 40
k=0
print('Top 5 actions: ')
#get the top-5 classified actions
for i in np.argsort(scores)[0][::-1][:5]:
print('%-22s %0.2f%%' % (action_label[str(i+1)], scores[0][i] * 100))
#this equation is used to print different actions on a separate line
y = y0 + k * dy
k+=1
cv2.putText(RGB_frame, text='{} - {:.2f}'.format(action_label[str(i+1)],scores[0][i]),
org=(5,y),fontFace=font, fontScale=1,
color=(0,0,255), thickness=2)
#save the frame in your current working directory
cv2.imwrite(current_dir + 'text_frame'+'.png', RGB_frame)
#plt.imshow(img)
#plt.show()
if __name__ == '__main__':
one_video()