chore: fix linting with black

Convolution Layer/classify.py

@@ -13,18 +13,15 @@
 
 # construct the argument parse and parse the arguments
 ap = argparse.ArgumentParser()
-ap.add_argument("-m", "--model", required=True,
-	help="path to trained model model")
-ap.add_argument("-l", "--labelbin", required=True,
-	help="path to label binarizer")
-ap.add_argument("-i", "--image", required=True,
-	help="path to input image")
+ap.add_argument("-m", "--model", required=True, help="path to trained model model")
+ap.add_argument("-l", "--labelbin", required=True, help="path to label binarizer")
+ap.add_argument("-i", "--image", required=True, help="path to input image")
 args = vars(ap.parse_args())
 
 # load the image
 image = cv2.imread(args["image"])
 output = imutils.resize(image, width=400)
- 
+
 # pre-process the image for classification
 image = cv2.resize(image, (96, 96))
 image = image.astype("float") / 255.0
@@ -44,18 +41,25 @@
 idxs = np.argsort(proba)[::-1][:2]
 
 # loop over the indexes of the high confidence class labels
-for (i, j) in enumerate(idxs):
-	# build the label and draw the label on the image
-	label = "{}: {:.2f}%".format(mlb.classes_[j], proba[j] * 100)
-	cv2.putText(output, label, (10, (i * 30) + 25), 
-		cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+for i, j in enumerate(idxs):
+    # build the label and draw the label on the image
+    label = "{}: {:.2f}%".format(mlb.classes_[j], proba[j] * 100)
+    cv2.putText(
+        output,
+        label,
+        (10, (i * 30) + 25),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.7,
+        (0, 255, 0),
+        2,
+    )
 
 # show the probabilities for each of the individual labels
-for (label, p) in zip(mlb.classes_, proba):
-	print("{}: {:.2f}%".format(label, p * 100))
+for label, p in zip(mlb.classes_, proba):
+    print("{}: {:.2f}%".format(label, p * 100))
 
 # show the output image
 cv2.imshow("Output", output)
 cv2.waitKey(0)
 
-# 0-striped 1-lining 2-square 3-dotted
+# 0-striped 1-lining 2-square 3-dotted

Convolution Layer/pyimagesearch/smallervggnet.py

@@ -9,59 +9,59 @@
 from keras.layers.core import Dense
 from keras import backend as K
 
+
 class SmallerVGGNet:
-	@staticmethod
-	def build(width, height, depth, classes, finalAct="softmax"):
-		# initialize the model along with the input shape to be
-		# "channels last" and the channels dimension itself
-		model = Sequential()
-		inputShape = (height, width, depth)
-		chanDim = -1
+    @staticmethod
+    def build(width, height, depth, classes, finalAct="softmax"):
+        # initialize the model along with the input shape to be
+        # "channels last" and the channels dimension itself
+        model = Sequential()
+        inputShape = (height, width, depth)
+        chanDim = -1
 
-		# if we are using "channels first", update the input shape
-		# and channels dimension
-		if K.image_data_format() == "channels_first":
-			inputShape = (depth, height, width)
-			chanDim = 1
+        # if we are using "channels first", update the input shape
+        # and channels dimension
+        if K.image_data_format() == "channels_first":
+            inputShape = (depth, height, width)
+            chanDim = 1
 
-		# CONV => RELU => POOL
-		model.add(Conv2D(32, (3, 3), padding="same",
-			input_shape=inputShape))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization(axis=chanDim))
-		model.add(MaxPooling2D(pool_size=(3, 3)))
-		model.add(Dropout(0.25))
+        # CONV => RELU => POOL
+        model.add(Conv2D(32, (3, 3), padding="same", input_shape=inputShape))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(3, 3)))
+        model.add(Dropout(0.25))
 
-		# (CONV => RELU) * 2 => POOL
-		model.add(Conv2D(64, (3, 3), padding="same"))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization(axis=chanDim))
-		model.add(Conv2D(64, (3, 3), padding="same"))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization(axis=chanDim))
-		model.add(MaxPooling2D(pool_size=(2, 2)))
-		model.add(Dropout(0.25))
+        # (CONV => RELU) * 2 => POOL
+        model.add(Conv2D(64, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(Conv2D(64, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Dropout(0.25))
 
-		# (CONV => RELU) * 2 => POOL
-		model.add(Conv2D(128, (3, 3), padding="same"))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization(axis=chanDim))
-		model.add(Conv2D(128, (3, 3), padding="same"))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization(axis=chanDim))
-		model.add(MaxPooling2D(pool_size=(2, 2)))
-		model.add(Dropout(0.25))
+        # (CONV => RELU) * 2 => POOL
+        model.add(Conv2D(128, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(Conv2D(128, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Dropout(0.25))
 
-		# first (and only) set of FC => RELU layers
-		model.add(Flatten())
-		model.add(Dense(1024))
-		model.add(Activation("relu"))
-		model.add(BatchNormalization())
-		model.add(Dropout(0.5))
+        # first (and only) set of FC => RELU layers
+        model.add(Flatten())
+        model.add(Dense(1024))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization())
+        model.add(Dropout(0.5))
 
-		# softmax classifier
-		model.add(Dense(classes))
-		model.add(Activation(finalAct))
+        # softmax classifier
+        model.add(Dense(classes))
+        model.add(Activation(finalAct))
 
-		# return the constructed network architecture
-		return model
+        # return the constructed network architecture
+        return model

multipupose-client/test1/client.py

@@ -1,25 +1,17 @@
 import socket
 
-
-s=socket.socket()
-
-s.connect(('127.0.0.1',8000))
-
+s = socket.socket()
+s.connect(("127.0.0.1", 8000))
 print("connecton")
- 
+
 while True:
-    a=input("first number")
-    msg=a
-    if msg=="back":
+    a = input("first number")
+    msg = a
+    if msg == "back":
         s.close()
         break
     s.send(msg.encode())
-    ms=s.recv(1024)
-    print("Answer:"+ms.decode())
-  #  msg=input("Bob:")
-  #  s.send(msg.encode())
-
-
-
-
-
+    ms = s.recv(1024)
+    print("Answer:" + ms.decode())
+#  msg=input("Bob:")
+#  s.send(msg.encode())

multipupose-client/test1/server.py

@@ -1,35 +1,34 @@
-import socket 
+import socket
 import logging
 import threading
 import time
+
+
 def thread(c):
     print("connection is established")
-    while(True):
+    while True:
         try:
-            msg=c.recv(1024)
+            msg = c.recv(1024)
             print(c)
             print(msg.decode())
-            f=int(msg.decode())
-            t=str(f*f)
+            f = int(msg.decode())
+            t = str(f * f)
             time.sleep(2)
             c.send(t.encode())
         except:
             c.close()
             break
 
-s=socket.socket()
-port=8000
-s.bind(('',port))
+
+s = socket.socket()
+port = 8000
+s.bind(("", port))
 print("server started")
 format = "%(asctime)s: %(message)s"
-while(True):
-    logging.basicConfig(format=format, level=logging.INFO,datefmt="%H:%M:%S")
+while True:
+    logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S")
     s.listen(6)
-    c,addr=s.accept()
+    c, addr = s.accept()
     x = threading.Thread(target=thread, args=(c,))
     x.start()
     print("connection is established")
-
-
-
-