structure updates

csv_img_to_tfrecord.py → csv_to_tfrecord.py

@@ -68,7 +68,7 @@ def create_tf_example(group, path, label_map_dict):
 def main():
     for directory in ['train','eval']:
 
-        image_path = os.path.join(os.getcwd(), 'data/{}'.format(directory))
+        image_path = os.path.join(os.getcwd(), 'data/{}/image's.format(directory))
         csv_path = os.path.join(os.getcwd(), 'data/{}_labels.csv'.format(directory,directory))
         output_path = os.path.join(os.getcwd(), 'data/{}.record'.format(directory))
         label_map_dict = label_map_util.get_label_map_dict(os.path.join(os.getcwd(), 'data/label_map.pbtxt'))

csv_to_yolo_txt.py

@@ -87,10 +87,9 @@ def main():
     CWD = os.getcwd()
 
     for directory in ['train','eval']:
-
         txt_path = 'data/{}/annotations/txt'.format(directory)
         csv_path = 'data/{}_labels.csv'.format(directory)
-        img_path = CWD + '/data/{}/'.format(directory)
+        img_path = CWD + '/data/{}/images/'.format(directory)
 
         if not os.path.exists(txt_path):
             os.makedirs(txt_path)
@@ -104,4 +103,4 @@ def main():
 
 if __name__ == '__main__':
     main()
-
+

egohands_dataset_cleaner.py

@@ -241,6 +241,14 @@ def final_finish():
     f = open("data/label_map.pbtxt","w")
     f.write("item {\n  id: 1\n  name: 'hand'\n}")
     f.close()
+    cwd = os.getcwd()
+    for directory in ['train','eval']:
+        src_dir = cwd+'/data/{}'.format(directory)
+        drc_dir = cwd+'/data/{}/images'.format(directory)
+        create_directory(drc_dir)
+        for file in os.listdir(src_dir):
+            if file.endswith(".jpg"):
+               sh.move(src_dir+'/'+file,drc_dir+'/'+file)
 
     sh.rmtree('egohands')
     #os.remove(EGO_HANDS_FILE)

model/tiny-yolo-handsnet.cfg

@@ -0,0 +1,139 @@
+[net]
+# Training
+# batch=64
+# subdivisions=2
+# Testing
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=30
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+[region]
+anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
+bias_match=1
+classes=1
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=0
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1