wip #78

Author: previ

MPU6050.py

@@ -1,13 +1,12 @@
-from __future__ import print_function
 """This program handles the communication over I2C
 between a Raspberry Pi and a MPU-6050 Gyroscope / Accelerometer combo.
 Made by: MrTijn/Tijndagamer
 Released under the MIT License
 Copyright 2015
 """
 
-import smbus2 as smbus
 import time
+import smbus2 as smbus
 
 class MPU6050:
 
@@ -88,10 +87,9 @@ def read_i2c_word(self, register):
 
         value = (high << 8) + low
 
-        if (value >= 0x8000):
+        if value >= 0x8000:
             return -((65535 - value) + 1)
-        else:
-            return value
+        return value
 
     # MPU-6050 Methods
 
@@ -120,7 +118,7 @@ def set_accel_range(self, accel_range):
         # Write the new range to the ACCEL_CONFIG register
         self.bus.write_byte_data(self.address, self.ACCEL_CONFIG, accel_range)
 
-    def read_accel_range(self, raw = False):
+    def read_accel_range(self, raw=False):
         """Reads the range the accelerometer is set to.
         If raw is True, it will return the raw value from the ACCEL_CONFIG
         register
@@ -141,10 +139,9 @@ def read_accel_range(self, raw = False):
                 return 8
             elif raw_data == self.ACCEL_RANGE_16G:
                 return 16
-            else:
-                return -1
+        return -1
 
-    def get_accel_data(self, g = False):
+    def get_accel_data(self, g=False):
         """Gets and returns the X, Y and Z values from the accelerometer.
         If g is True, it will return the data in g
         If g is False, it will return the data in m/s^2
@@ -174,13 +171,11 @@ def get_accel_data(self, g = False):
         y = y / accel_scale_modifier
         z = z / accel_scale_modifier
 
-        if g is True:
-            return {'x': x, 'y': y, 'z': z}
-        elif g is False:
+        if g is False:
             x = x * self.GRAVITIY_MS2
             y = y * self.GRAVITIY_MS2
             z = z * self.GRAVITIY_MS2
-            return {'x': x, 'y': y, 'z': z}
+        return {'x': x, 'y': y, 'z': z}
 
     def set_gyro_range(self, gyro_range):
         """Sets the range of the gyroscope to range.
@@ -193,7 +188,7 @@ def set_gyro_range(self, gyro_range):
         # Write the new range to the ACCEL_CONFIG register
         self.bus.write_byte_data(self.address, self.GYRO_CONFIG, gyro_range)
 
-    def read_gyro_range(self, raw = False):
+    def read_gyro_range(self, raw=False):
         """Reads the range the gyroscope is set to.
         If raw is True, it will return the raw value from the GYRO_CONFIG
         register.
@@ -214,8 +209,7 @@ def read_gyro_range(self, raw = False):
                 return 1000
             elif raw_data == self.GYRO_RANGE_2000DEG:
                 return 2000
-            else:
-                return -1
+        return -1
 
     def get_gyro_data(self):
         """Gets and returns the X, Y and Z values from the gyroscope.
@@ -249,9 +243,9 @@ def get_gyro_data(self):
 
     def get_all_data(self):
         """Reads and returns all the available data."""
-        temp = get_temp()
-        accel = get_accel_data()
-        gyro = get_gyro_data()
+        temp = self.get_temp()
+        accel = self.get_accel_data()
+        gyro = self.get_gyro_data()
 
         return [accel, gyro, temp]
 
@@ -267,4 +261,4 @@ def get_all_data(self):
         if abs(gyro_data['z']) > mpu.GYRO_THRESHOLD_Z:
             dz = gyro_data['z'] * dt
             z = z + dz
-            print( "z: ", z, end="\r")
+            print("z: ", z, end="\r")

cnn_classifier.py

@@ -20,14 +20,8 @@
 This module implements the CNNClassifier class, which is the interface for
 using an existing and trained CNN model.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import time
 import logging
 
-import operator
 import numpy as np
 import tensorflow as tf
 
@@ -39,17 +33,17 @@ def __init__(self, model_file, label_file, input_layer="input", output_layer="fi
         self._labels = self.load_labels(label_file)
         input_name = "import/" + input_layer
         output_name = "import/" + output_layer
-        self._input_operation = self._graph.get_operation_by_name(input_name);
-        self._output_operation = self._graph.get_operation_by_name(output_name);
+        self._input_operation = self._graph.get_operation_by_name(input_name)
+        self._output_operation = self._graph.get_operation_by_name(output_name)
         self._session = tf.Session(graph=self._graph)
         self._graph_norm = tf.Graph()
         with self._graph_norm.as_default():
             image_mat = tf.placeholder(tf.float32, None, name="image_rgb_in")
             float_caster = tf.cast(image_mat, tf.float32)
-            dims_expander = tf.expand_dims(float_caster, 0);
+            dims_expander = tf.expand_dims(float_caster, 0)
             resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
             normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std], name="image_norm_out")
-            self._input_operation_norm =  self._graph_norm.get_operation_by_name("image_rgb_in")
+            self._input_operation_norm = self._graph_norm.get_operation_by_name("image_rgb_in")
             self._output_operation_norm = self._graph_norm.get_operation_by_name("image_norm_out")
         self._sess_norm = tf.Session(graph=self._graph_norm)
 
@@ -75,19 +69,16 @@ def read_tensor_from_image_file(self, file_name, input_height=299, input_width=2
         file_reader = tf.read_file(file_name, input_name)
 
         if file_name.endswith(".png"):
-            image_reader = tf.image.decode_png(file_reader, channels = 3,
-                                       name='png_reader')
+            image_reader = tf.image.decode_png(file_reader, channels=3, name='png_reader')
         elif file_name.endswith(".gif"):
-            image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
-                                                  name='gif_reader'))
+            image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name='gif_reader'))
         elif file_name.endswith(".bmp"):
             image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
         else:
-            image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
-                                        name='jpeg_reader')
+            image_reader = tf.image.decode_jpeg(file_reader, channels=3, name='jpeg_reader')
 
         float_caster = tf.cast(image_reader, tf.float32)
-        dims_expander = tf.expand_dims(float_caster, 0);
+        dims_expander = tf.expand_dims(float_caster, 0)
         resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
         normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
         sess = tf.Session()
@@ -111,20 +102,15 @@ def load_labels(self, label_file):
     def classify_image(self,
                        image_file_or_mat,
                        top_results=3):
-        s_t = time.time()
         t = None
-        if type(image_file_or_mat) == str:
+        if isinstance(image_file_or_mat, str):
             t = self.read_tensor_from_image_file(file_name=image_file_or_mat)
         else:
             t = self.read_tensor_from_image_mat(image_file_or_mat)
 
-        #logging.info( "time.norm: " + str(time.time() - s_t))
-        s_t = time.time()
 
         results = self._session.run(self._output_operation.outputs[0],
-                                        {self._input_operation.outputs[0]: t})
-
-        #logging.info( "time.cls: " + str(time.time() - s_t))
+                                    {self._input_operation.outputs[0]: t})
 
         top_results = min(top_results, len(self._labels))
         results = np.squeeze(results)

cnn_manager.py

@@ -73,8 +73,8 @@ def delete_model(self, model_name):
             try:
                 os.remove(MODEL_PATH + "/" + model_name + ".pb")
                 os.remove(MODEL_PATH + "/" + model_name + ".txt")
-            except:
-                logging.warning("model files not found: " + model_name)
+            except Exception:
+                logging.warning("model files not found: %s", model_name)
             del self._models[model_name]
             self._save_model_meta()
 
@@ -103,12 +103,12 @@ def wait_train_jobs(self):
     def load_model(self, model_name):
         model_info = self._models.get(model_name)
         if model_info:
-            return CNNClassifier(model_file = MODEL_PATH + "/" + model_name + ".pb",
-                                 label_file = MODEL_PATH + "/" + model_name + ".txt",
+            return CNNClassifier(model_file=MODEL_PATH + "/" + model_name + ".pb",
+                                 label_file=MODEL_PATH + "/" + model_name + ".txt",
                                  output_layer=model_info["output_layer"],
-                                 input_height = int(model_info["image_height"]),
-                                 input_width = int(model_info["image_width"]))
-
+                                 input_height=int(model_info["image_height"]),
+                                 input_width=int(model_info["image_width"]))
+        return None
     class TrainThread(threading.Thread):
 
         def __init__(self, manager, model_name, architecture, image_tags, photos_metadata, training_steps, learning_rate):