Rename HumanMotionsClassificationZiad.py to Train.py

HumanMotionsClassificationZiad.py → Train.py

@@ -364,92 +364,6 @@ torch.save(model.state_dict(), 'model')
 print('TRAINING COMPLETE')
 
 
-# In[ ]:
-
-
-#Testing.....................................
-
-
-# In[43]:
-
-
-import torch
-import numpy as np
-import argparse
-import joblib
-import cv2
-import torch.nn as nn
-import torch.nn.functional as F
-import time
-import albumentations
-from torchvision.transforms import transforms   
-from torch.utils.data import Dataset, DataLoader
-from PIL import Image
-
-
-# In[44]:
-
-
-aug = albumentations.Compose([
-    albumentations.Resize(224, 224),
-    ])
-
-
-# In[ ]:
-
-
-
-
-
-# In[51]:
-
-
-import cv2
-cap = cv2.VideoCapture(r'C:\Users\abdul\Desktop\Research\work\mhamad syrian\ziad\dataVideos\bend\daria_bend.avi')
-if (cap.isOpened() == False):
-    print('Error while trying to read video. 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(str('output'), cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width,frame_height))
-
-
-# In[52]:
-
-
-# read until end of video
-while(cap.isOpened()):
-    # capture each frame of the video
-    ret, frame = cap.read()
-    if ret == True:
-        model.eval()
-        with torch.no_grad():
-            # conver to PIL RGB format before predictions
-            pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-            pil_image = aug(image=np.array(pil_image))['image']
-            pil_image = np.transpose(pil_image, (2, 0, 1)).astype(np.float32)
-            pil_image = torch.tensor(pil_image, dtype=torch.float).cuda()
-            pil_image = pil_image.unsqueeze(0)
-            
-            outputs = model(pil_image)
-            _, preds = torch.max(outputs.data, 1)
-        
-        cv2.putText(frame, lb.classes_[preds], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 200, 0), 2)
-        cv2.imshow('image', frame)
-        out.write(frame)
-        # press `q` to exit
-        if cv2.waitKey(27) & 0xFF == ord('q'):
-            break
-    else: 
-        break
-# release VideoCapture()
-cap.release()
-# close all frames and video windows
-cv2.destroyAllWindows()
-
-
-# In[ ]: