fix #98

Author: previ

camera.py

@@ -153,6 +153,8 @@ def photo_take(self):
         im_pil.resize(PHOTO_THUMB_SIZE).save(oft)
         self._photos.append({"name":filename})
         self.save_photo_metadata()
+        of.close()
+        oft.close()
 
     def is_recording(self):
         return self.recording
@@ -184,6 +186,7 @@ def video_rec(self, video_name=None):
         self.save_photo_metadata()
         self._camera.video_rec(PHOTO_PATH + "/" + filename)
         self.video_start_time = time.time()
+        oft.close()
 
     def video_stop(self):
         if self.recording:

cnn_manager.py

@@ -50,7 +50,7 @@ def __init__(self):
         try:
             f = open(MODEL_METADATA, "r")
             self._models = json.load(f)
-            f.close
+            f.close()
         except IOError:
             self._models = {}
             self._save_model_meta()
@@ -66,7 +66,7 @@ def get_model_status(self, model_name):
     def _save_model_meta(self):
         f = open(MODEL_METADATA, "w")
         json.dump(self._models, f)
-        f.close
+        f.close()
 
     def delete_model(self, model_name):
         if self._models.get(model_name):

cnn_models/models.json

@@ -1 +1 @@
-{}
+{}

cnn_train.py

@@ -908,7 +908,7 @@ def add_input_distortions(self, flip_left_right, random_crop, random_scale,
         precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32)
         precropped_image = tf.image.resize_bilinear(decoded_image_4d,
                                                     precrop_shape_as_int)
-        precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0])
+        precropped_image_3d = tf.squeeze(precropped_image, axis=[0])
         cropped_image = tf.random_crop(precropped_image_3d,
                                        [input_height, input_width, input_depth])
         if flip_left_right:
@@ -994,7 +994,7 @@ def add_final_training_ops(self, class_count, final_tensor_name, bottleneck_tens
         tf.summary.histogram('activations', final_tensor)
 
         with tf.name_scope('cross_entropy'):
-            cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
+            cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
                 labels=ground_truth_input, logits=logits)
             with tf.name_scope('total'):
                 cross_entropy_mean = tf.reduce_mean(cross_entropy)

config.py

@@ -33,6 +33,7 @@ def get(cls):
     def read(cls):
         f = open(CONFIG_FILE, 'r')
         cls._config = json.load(f)
+        f.close()
         return cls._config
 
     @classmethod

cv/image.py

@@ -169,7 +169,7 @@ def find_blobs(self, minsize=0, maxsize=10000000):
         for c in contours:
             area = cv2.contourArea(c)
             if area > minsize and area < maxsize:
-                if len(blobs) and area > blobs[0].area:
+                if len(blobs) and area > blobs[0].area():
                     blobs.insert(0, blob.Blob(c))
                 else:
                     blobs.append(blob.Blob(c))

photos/metadata.json

@@ -1 +1 @@
-[]
+[{"name": "DSC2.jpg"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}, {"name": "VIDvideo_test.mp4"}]

photos/photos/0.txt

@@ -0,0 +1 @@
+placeholder