refactor: remove unnecessary whitespace

Blank lines should not contain any tabs or spaces.

Author: deepsource-autofix[bot]

auctionhouse.py

@@ -39,7 +39,7 @@
 
     for ki,index in enumerate(indices): # ki is index of the kernel so far
         slice_conv[x] += slice_g[index] * k[ki] 
-    
+
     # print(slice_g[x], slice_conv[x])
 
 # find transitions
@@ -72,7 +72,7 @@
         # count += 1
     elif val == 1 and startx > -1: # not the first one
         endx = i
-        
+
         if endx - startx > 200:
             extents.append([startx+35,endx]) # add 35 to account for offset
             # TODO change this so it throws away first two points
@@ -173,7 +173,7 @@
                                            total_num_peaks=1)    
     section[2] = cv2.circle(section[2], (cx[0],cy[0]), radii[0]+1, 1, -1)
 
-    
+
 # erode for better OCR
 for section in sections:
     section[0] = cv2.erode(section[0], np.ones((2,2), np.uint8), iterations=1)
@@ -189,7 +189,7 @@
     section[5] = cv2.morphologyEx(section[5], cv2.MORPH_CLOSE, np.ones((3,3)))
     plt.hist(section[5], [0,50,100,150,200,255])
     plt.show()
-        
+
 
 
 # show sections
@@ -205,17 +205,16 @@
     overall = pytesseract.image_to_string(Image.fromarray(section[3].astype(np.uint8)),config=tessdata_dir_config)
     position = pytesseract.image_to_string(Image.fromarray(section[4].astype(np.uint8)),config=tessdata_dir_config)
     name = pytesseract.image_to_string(Image.fromarray(section[5].astype(np.uint8)),config=tessdata_dir_config)
-    
+
     message = f"{overall} {name} who plays {position} has {timestamp} left on auction. Winning Bid is {winning_bid} while BIN is {buy_it_now}"
     alt_message = f"""Timestamp: {timestamp}
     Winning Bid: {winning_bid}
     Buy it Now: {buy_it_now}
     Overall: {overall}
     Position: {position}
     Name: {name}"""
-                      
+
     print(alt_message)
-    
+
     plt.show()
 
-    

efficientnet_test.py

@@ -15,7 +15,7 @@
 
 def load_data():
     image = imread("./samples/2021-05-02_17;54;46/img_191.png")
-    
+
     plt.figure(figsize=(10, 10))
     plt.imshow(image)
     plt.show()

map_analysis.py

@@ -63,6 +63,6 @@ def analyze(img):
     plt.imshow(img, cmap="gray")
     plt.scatter([point[0] for point in circle_points], [point[1] for point in circle_points])
     plt.hist(img.ravel(), 256, (0,256))
-    
+
     plt.show()
     i+=20

ocrtest.py

@@ -14,7 +14,7 @@ def get_grayscale(image):
 # noise removal
 def remove_noise(image):
     return cv2.medianBlur(image,5)
- 
+
 #thresholding
 def thresholding(image):
     return cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
@@ -23,7 +23,7 @@ def thresholding(image):
 def dilate(image):
     kernel = np.ones((5,5),np.uint8)
     return cv2.dilate(image, kernel, iterations = 1)
-    
+
 #erosion
 def erode(image):
     kernel = np.ones((5,5),np.uint8)
@@ -82,4 +82,4 @@ def preprocess_img(img):
     img =  preprocess_img(img)
 
     print(pytesseract.image_to_string(img,config=tessdata_dir_config))
-    
+

openvino_test.py

@@ -24,7 +24,7 @@ def start():
 
     net = ie.read_network(model="model/road-segmentation-adas-0001.xml")
     exec_net = ie.load_network(net, "CPU")
-    
+
     output_layer_ir = next(iter(exec_net.outputs))
     input_layer_ir = next(iter(exec_net.input_info))
 
@@ -66,46 +66,46 @@ def inference(image, ie, net, exec_net, output_layer_ir, input_layer_ir, return_
     # image = cv2.imread("data/empty_road_mapillary.jpg")
     # image = read_img_from_num(int(input("Image number: ")))
     # print(type(image))
-    
+
     rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image_h, image_w, _ = image.shape
-    
+
     # N,C,H,W = batch size, number of channels, height, width
     N, C, H, W = net.input_info[input_layer_ir].tensor_desc.dims
-    
+
     # OpenCV resize expects the destination size as (width, height)
     resized_image = cv2.resize(image, (W, H))
-    
+
     # reshape to network input shape
     input_image = np.expand_dims(
         resized_image.transpose(2, 0, 1), 0
     )  
     # plt.imshow(rgb_image)
     # plt.show()
-    
+
     # Run the infernece
     result = exec_net.infer(inputs={input_layer_ir: input_image})
     result_ir = result[output_layer_ir]
-    
+
     # Prepare data for visualization
     segmentation_mask = np.argmax(result_ir, axis=1)
     # plt.imshow(segmentation_mask[0])
     # plt.show()
- 
+
     img = segmentation_mask[0]
 
     if return_image:
         resized_image[img == 0] = [0, 0, 0]
         return resized_image
 
     x, y = find_xy(img)
-    
+
     # find first index where x = -1
     for i, xval in enumerate(x):
         if xval == -1:
             last = i
             break
-    
+
     x = x[2:last - 1] # removing first two because they are usually outliers. 
     y = y[2:last - 1] #z this can be manually tuned, for start and end
 
@@ -127,7 +127,7 @@ def inference(image, ie, net, exec_net, output_layer_ir, input_layer_ir, return_
     # COMMENT ME OUT NOTE TODO when using with play.py
     # for i, item in enumerate(x):
     #     print(f"{item}\t{y[i]}")
-    
+
     try:
         slope = (yslope[-1] - yslope[0])/(x[-1] - x[0])
     except:
@@ -146,10 +146,10 @@ def inference(image, ie, net, exec_net, output_layer_ir, input_layer_ir, return_
         angle = -1 * (90+a)/90
     else:
         return "error"
-    
+
     # angle = (x[0] - x[-1])/(y[0] - y[-1]) / 5 # NOTE This parameter should be manually tuned
     #print(angle)
-    
+
     # has to be commented out when running in play.py
     xr = list(reversed(x))
     yr = list(reversed(y))         

play.py

@@ -157,10 +157,10 @@ def cv_act_racing(self, img):
                 print(f"ERROR: Last angle is used: {-self.lastvalue}")
                 self.control_racing(joystick)
                 return
-            
+
             angle *= self.anglefactor
             self.lastvalue = angle
-            
+
             print(angle)
             if angle > self.cutoff:
                 angle = self.cutoff
@@ -169,7 +169,7 @@ def cv_act_racing(self, img):
             joystick = [angle, 0.3]
 
             self.control_racing(joystick)
-        
+
     def act(self, img):
 
         ## determine manual override
@@ -182,7 +182,7 @@ def act(self, img):
             ## Think
             # joystick = self.model.predict(vec, batch_size=1)[0]
             joystick = categorical_model_predict(self.model, vec)
-            
+
             print(joystick)
 
             # if len(joystick) == 8:
@@ -200,7 +200,7 @@ def act(self, img):
 
             self.control(joystick)
 
-        
+
         ## Act
         ## has been put in Think block NOTE 
 
@@ -214,7 +214,7 @@ def act(self, img):
     gpus = tf.config.list_physical_devices("GPU")
     for gpu in gpus:
         tf.config.experimental.set_memory_growth(gpu,True)
-    
+
     # turn on program and let it run forever
     actor = Actor()
     screenshot = Screenshotter()

telemetry.py

@@ -19,10 +19,10 @@ def save_waypoints():
     except KeyboardInterrupt:
         print("CtrlC detected, stopping ands saving")
         np.savetxt("waypoints.txt", vals)
-    
+
 def get_waypoints(print_values=False):
     generator = return_vals(PORT_NUMBER)
-    
+
     for i in generator:
         if print_values:
             print(i)
@@ -84,7 +84,7 @@ def follow_waypoints(waypoints_file="waypoints.txt"):
         except KeyboardInterrupt:
             print("Stopping due to Ctrl C Event")
             break
-    
+
 
 
 if __name__ == "__main__":
@@ -97,4 +97,3 @@ def follow_waypoints(waypoints_file="waypoints.txt"):
     #    # time.sleep(1) 
 
     follow_waypoints()
-        

train.py

@@ -2,7 +2,7 @@
 
 
 import numpy as np
-    
+
 import tensorflow as tf
 
 from tensorflow.keras.models import Sequential
@@ -204,7 +204,7 @@ def train_sequence_categorical_model(x_train, y_train, _model=sequence_categoric
 
     # Train model
     model = _model()
-    
+
     checkpoint = ModelCheckpoint("model_weights_seq_c1.h5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')
     callbacks_list = [checkpoint]
 
@@ -245,7 +245,7 @@ def autoencoder_model():
     y = Conv2DTranspose(filters=32, kernel_size=3, strides=2,
                         name="deconv2d_5")(y)
     y = Conv2DTranspose(filters=1, kernel_size=3, strides=2, name="img_out")(y)
-    
+
     x = Flatten(name='flattened')(x)
     x = Dense(256, activation='relu')(x)
     x = Dropout(drop)(x)
@@ -255,7 +255,7 @@ def autoencoder_model():
     x = Dropout(drop)(x)
 
     angle_out = Dense(15, activation='softmax', name='angle_out')(x)
-        
+
     model = Model(inputs=[img_in], outputs=[angle_out], name="latent")
     return model
 
@@ -270,7 +270,7 @@ def train_model(x_train, y_train, _model=create_model, batch_size=128, epochs=10
 
     checkpoint = ModelCheckpoint("model_weights_bal.h5", monitor='val_loss', verbose=1, save_best_only=True, mode='min')
     callbacks_list = [checkpoint]
-    
+
     # model.compile(loss=customized_loss, optimizer="adam")
     # model.compile(loss="mean_squared_error", optimizer="adam")
     model.compile(loss="mean_squared_error", optimizer=optimizers.SGD(lr=0.1))
@@ -328,7 +328,7 @@ def train_autoencoder_model(x_train, y_train, _model=autoencoder_model, batch_si
     # tf.keras.utils.plot_model(sequence_categorical_model(), to_file='model.png', show_shapes=True)
     # exit()
 
-    
+
     # Load Training Data
     print("loading training data")
     # x_train = np.load("data/x_sbal.npy")
@@ -348,4 +348,3 @@ def train_autoencoder_model(x_train, y_train, _model=autoencoder_model, batch_si
     train_categorical_model(x_train, y_train, _model=categorical_model, batch_size=batch_size, epochs=epochs)
     # train_autoencoder_model(x_train, y_train, _model=autoencoder_model, batch_size=batch_size, epochs=epochs)
     # train_sequence_categorical_model(x_train, y_train, _model=sequence_categorical_model, batch_size=batch_size, epochs=epochs)
-