input preprocess script added. lanenet connection added.

get_video.py

@@ -4,12 +4,15 @@
 from keras.utils.np_utils import to_categorical
 import random
 
+#from lanenet_repo.tools import test_lanenet
+
 ROOT_PATH = ''
 
 
 def get_video_frames(src, fpv, frame_height, frame_width):
     # print('reading video from', src)
     cap = cv2.VideoCapture(src)
+    #filename = src.split('/')[3][:-4]
 
     frames = []
     if not cap.isOpened():
@@ -30,7 +33,6 @@ def get_video_frames(src, fpv, frame_height, frame_width):
     rnd_frame = frames[rnd_idx]
     rnd_frame = cv2.resize(rnd_frame,(224,224)) # Needed for Densenet121-2d
 
-    # Return fpv=10 frames
     step = len(frames)//fpv
 
     # get 16 frames from each half
@@ -46,6 +48,15 @@ def get_video_frames(src, fpv, frame_height, frame_width):
     for af in avg_frames:
         rsz_f = cv2.resize(af, (frame_width, frame_height))
         avg_resized_frames.append(rsz_f)
+
+    # call lanenet for lane detection!
+    #avg_resized_lanenet_frames = []
+    #weights_path = '/home/yagiz/Sourcebox/git/T3D-keras/lanenet_repo/model/tusimple_lanenet_vgg/tusimple_lanenet_vgg.ckpt'
+    #counts = 0
+    #for image in avg_resized_frames:
+    #    lanenet_frame = test_lanenet.test_lanenet(image, weights_path, filename, counts)
+    #    counts += 1
+    #    avg_resized_lanenet_frames.append(lanenet_frame)
     return np.asarray(rnd_frame)/255.0,np.asarray(avg_resized_frames)
 
 
@@ -61,7 +72,6 @@ def get_video_and_label(index, data, frames_per_video, frame_height, frame_width
     # print('Frame shape',frame.shape)
     # print('Clip shape',clip.shape)
 
-
     return frame, clip, behavior_class
 
 
@@ -86,9 +96,11 @@ def video_gen(data, frames_per_video, frame_height, frame_width, channels, num_c
                     i, data, frames_per_video, frame_height, frame_width)
 
                 # Appending them to existing batch
-                frame = np.append(frame, single_frame, axis=0)
-                clip = np.append(clip, single_clip, axis=0)
-
+                try:
+                    frame = np.append(frame, single_frame, axis=0)
+                    clip = np.append(clip, single_clip, axis=0)
+                except Exception as e:
+                    print(e, single_frame.shape, single_clip.shape, frame.shape, clip.shape)
                 y_train = np.append(y_train, [behavior_class])
             y_train = to_categorical(y_train, num_classes=num_classes)
 

preprocess.py

@@ -0,0 +1,29 @@
+import os
+import cv2
+import numpy as np
+
+def cutter(src):
+
+    cap = cv2.VideoCapture(src)
+
+    frames = []
+    if not cap.isOpened():
+        cap.open(src)
+    ret = True
+    while(True and ret):
+        # Capture frame-by-frame
+        ret, frame = cap.read()
+
+        frames.append(frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # When everything done, release the capture
+    cap.release()
+
+    croped_frames = []
+    for cf in frames:
+        rsz_f = cf[300:950, 0:1920]  # cut unnecessary sky and car view
+        croped_frames.append(rsz_f)
+
+    return np.asarray(croped_frames)

train_T3D_keras.py

@@ -20,7 +20,7 @@
 FRAME_CHANNEL = 3
 NUM_CLASSES = 2
 BATCH_SIZE = 2
-EPOCHS = 10
+EPOCHS = 5
 MODEL_FILE_NAME = 'T3D_saved_model.h5'