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3dcnn.py
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3dcnn.py
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import numpy as np
import torch
import torch.nn as nn
import os, cv2, sys
import pandas as pd
from ast import literal_eval
from tqdm import tqdm
sys.path.append('../')
from resnetpytorch.models import resnet
from torchvision import transforms
def create_clips(cap, index):
fourcc = cv2.VideoWriter_fourcc(*'MPEG')
out = cv2.VideoWriter('/Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/clips/output_' + str(index) + '.avi', fourcc, 5.0, (512,384), 0)
for i in range(5):
_, frame = cap.read()
frame = cv2.resize(frame, (512, 384))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
out.write(frame)
cap.set(cv2.CAP_PROP_POS_FRAMES,150+index-4)
out.release()
if __name__ == '__main__':
df = pd.read_csv('/Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/gaze_file.csv').to_numpy()
file3d = '/Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/clips/output_0.avi'
cap = cv2.VideoCapture(file3d)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(frame_count)
model = resnet.generate_model(50)
transforms = transforms.Compose([transforms.ToTensor()])
print(model)
last = None
_, frame = cap.read()
# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
last = frame
last = transforms(last)
last = last.unsqueeze(dim=3)
print(last.shape)
for i in range(4):
print(i)
_, frame = cap.read()
# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# frame = np.expand_dims(frame, axis=2)
frame = transforms(frame)
frame = frame.unsqueeze(dim=3)
last = torch.cat((last, frame), axis=3)
# cv2.imshow('img', frame)
# cv2.waitKey(0)
# last = np.expand_dims(last, axis=0)
# last = transforms(last)
last = last.unsqueeze(dim=0)
# last = last.unsqueeze(dim=4)
print(last.shape)
x = model(last)
print(x.shape)
# file = '/Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/scenevideo.mp4'
# cap = cv2.VideoCapture(file)
# frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# trim_size = 150
# _ = os.system('rm -r /Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/clips/')
# _ = os.system('mkdir /Users/sanketsans/Downloads/Pavis_Social_Interaction_Attention_dataset/test_BookShelf_S1/clips/')
# cap.set(cv2.CAP_PROP_POS_FRAMES,150-4)
# for i in tqdm(range(frame_count-300)):
# create_clips(cap, i)
# ret, frame = cap.read()
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# pts = [0.5, 0.5]
# try:
# gpts = list(map(literal_eval, df[trim_size+i, 1:]))
# avg = [sum(y) / len(y) for y in zip(*gpts)]
#
# start_point = (int(pts[0]*frame.shape[1]) - 100, int(pts[1]*frame.shape[0]) + 100)
# end_point = (int(pts[0]*frame.shape[1]) + 100, int(pts[1]*frame.shape[0]) - 100)
# pred_start_point = (int(avg[0]*frame.shape[1]) - 100, int(avg[1]*frame.shape[0]) + 100)
# pred_end_point = (int(avg[0]*frame.shape[1]) + 100, int(avg[1]*frame.shape[0]) - 100)
#
# frame = cv2.circle(frame, (int(pts[0]*1920),int(pts[1]*1080)), radius=5, color=(0, 0, 255), thickness=5)
# frame = cv2.circle(frame, (int(avg[0]*1920),int(avg[1]*1080)), radius=5, color=(0, 255, 0), thickness=5)
#
# frame = cv2.rectangle(frame, start_point, end_point, color=(0, 0, 255), thickness=5)
# frame = cv2.rectangle(frame, pred_start_point, pred_end_point, color=(0, 255, 0), thickness=5)
# except:
# pass
# cv2.imshow('image', frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break