realtime.py

#!/usr/bin/env python
# coding: utf-8

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#!/usr/bin/env python
# coding: utf-8

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'''
USAGE:
python cam_test.py 
'''
import torch
import joblib
import torch.nn as nn
import numpy as np
import cv2
import torch.nn.functional as F
import time
import cnn_models
 
# load label binarizer
lb = joblib.load('C:\\Users\\SARAH\\Desktop\\Multivariate Stats\\ASL\\output\\lb.pkl')
model = cnn_models.CustomCNN()
model.load_state_dict(torch.load('C:\\Users\\SARAH\\Desktop\\Multivariate Stats\\ASL\\output\\model.pth'))
print(model)
print('Model loaded')


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def hand_area(img):
    hand = img[100:324, 100:324]
    hand = cv2.resize(hand, (224,224))
    return hand


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cap = cv2.VideoCapture(0)
if (cap.isOpened() == False):
    print('Error while trying to open camera. Plese check again...')
# get the frame width and height
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
# define codec and create VideoWriter object
out = cv2.VideoWriter('ASL/output/asl.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width,frame_height))


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while(cap.isOpened()):
    # capture each frame of the video
    ret, frame = cap.read()
    # get the hand area on the video capture screen
    cv2.rectangle(frame, (100, 100), (324, 324), (20, 34, 255), 2)
    hand = hand_area(frame)
    image = hand
    
    image = np.transpose(image, (2, 0, 1)).astype(np.float32)
    image = torch.tensor(image, dtype=torch.float)
    image = image.unsqueeze(0)
    
    outputs = model(image)
    _, preds = torch.max(outputs.data, 1)
    
    cv2.putText(frame, lb.classes_[preds], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
    #plt.imshow(frame, cmap = 'gray', interpolation = 'bicubic')
    #plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    #plt.show()
    cv2.imshow('image', frame)
    #exit()
    #out.write(frame)
    # press `q` to exit
    if cv2.waitKey(27) & 0xFF == ord('q'):
        break
# release VideoCapture()
cap.release()
# close all frames and video windows
cv2.destroyAllWindows()