style: format code with autopep8

Format code with autopep8

This commit fixes the style issues introduced in dcf9821 according to the output
from Autopep8.

Details: https://app.deepsource.com/gh/avinashkranjan/Amazing-Python-Scripts/transform/5d389ae7-399c-4367-8872-63d7f8b60b9a/

Author: deepsource-autofix[bot]

AirObjects/air.py

@@ -5,13 +5,13 @@
 # cap = cv2.VideoCapture('NASA_video1.mp4') # video with airplane.
 
 # patch_size = 15 # Size of image patch to extract around featuns points
-patch_size = 25 # Size of image patch to extract around feature points
+patch_size = 25  # Size of image patch to extract around feature points
 
 
-count      = 0  # Loop counter to control frequency of object recognition
-objfreq    = 5  # Frequence of object recognition
+count = 0  # Loop counter to control frequency of object recognition
+objfreq = 5  # Frequence of object recognition
 # NumCorners = 50 # Number of corners to extract in a given frame
-NumCorners = 10 # Number of corners to extract in a given frame
+NumCorners = 10  # Number of corners to extract in a given frame
 '''
 # fourcc = cv2.cv.CV_FOURCC(*'XVID')
 # out = cv2.VideoWriter('result.avi', fourcc, 20.0, (450,170))
@@ -32,31 +32,30 @@
 if 1:
     if 1:
         # Convert to gray scale
-        gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
         # Find corners in gray scale image
-        corners = cv2.goodFeaturesToTrack(gray,NumCorners,0.01,100)
-        print 'The corners are:',corners
+        corners = cv2.goodFeaturesToTrack(gray, NumCorners, 0.01, 100)
+        print 'The corners are:', corners
         corners = np.int0(corners)
 
         # For each corner found, extract a patch and classify patch
-        for j,i in enumerate(corners):
-            x,y = i.ravel()
-            #cv2.circle(frame,(x,y),3,255,-1)
+        for j, i in enumerate(corners):
+            x, y = i.ravel()
+            # cv2.circle(frame,(x,y),3,255,-1)
             print 'The x pos of the corner is: ', x
             print 'The y pos of the corner is: ', y
             print 'The i of the corners is: ', i
             print 'The j of the corners is: ', j
             # Define size of patch in image coordinates
             xstart = x - patch_size
-            xend   = x + patch_size
+            xend = x + patch_size
             ystart = y - patch_size
-            yend   = y + patch_size
-            
+            yend = y + patch_size
 
             # clip image patch based on image size
-            xlen   = frame.shape[1]
-            ylen   = frame.shape[0]
+            xlen = frame.shape[1]
+            ylen = frame.shape[0]
 
             if xend > xlen:
                 xend = xlen
@@ -67,48 +66,50 @@
                 yend = ylen
             if ystart < 0:
                 ystart = 0
-                
-            cv2.rectangle(frame,(xstart,ystart),(xend,yend),(255,0,0),2)
-            # Extract the image patch from each frame in the video
-            img_patch         = frame[ystart:yend,xstart:xend]
 
+            cv2.rectangle(frame, (xstart, ystart),
+                          (xend, yend), (255, 0, 0), 2)
+            # Extract the image patch from each frame in the video
+            img_patch = frame[ystart:yend, xstart:xend]
 
             # Transform image to use caffe library
             transformed_image = transformer.preprocess('data', img_patch)
-        
+
             # copy the image data into the memory allocated for the net
-            net.blobs['data'].data[j,:,:,:] = transformed_image
-    
-        ### perform classification
+            net.blobs['data'].data[j, :, :, :] = transformed_image
+
+        # perform classification
         output = net.forward()
-    
+
         # Go through image patch for each corner and find if there are any airplanes
         Position = []
-        for i,j in enumerate(corners):
-            x,y = j.ravel()
-            output_prob = output['prob'][i] 
-    
+        for i, j in enumerate(corners):
+            x, y = j.ravel()
+            output_prob = output['prob'][i]
+
             # sort top five predictions from softmax output
             # top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items
-            top_inds = output_prob.argsort()[::-1][:10]  # reverse sort and take five largest items
-            print 'The classes are:', top_inds 
+            # reverse sort and take five largest items
+            top_inds = output_prob.argsort()[::-1][:10]
+            print 'The classes are:', top_inds
             # print 'predicted class is:', output_prob.argmax()
-            # print 'output label:', labels[output_prob.argmax()]    
+            # print 'output label:', labels[output_prob.argmax()]
             # print 'prob', output_prob[top_inds[0]]
 
             # If airlane, record position to draw bounding box
 
-            AirplaneLabels = [895,404,405,812]  # Airplane label ids in caffe database
-            #437,566,556,570,706,735,752,818,830,848
-            #VehicleLabels = [867,717,675,757,569,734,751,817,864,656] # Car, truck, van label ids in caffe database
+            # Airplane label ids in caffe database
+            AirplaneLabels = [895, 404, 405, 812]
+            # 437,566,556,570,706,735,752,818,830,848
+            # VehicleLabels = [867,717,675,757,569,734,751,817,864,656] # Car, truck, van label ids in caffe database
             # for k in range (0,5):
-            for k in range (0,10):
-                if (top_inds[k] in AirplaneLabels ):
+            for k in range(0, 10):
+                if (top_inds[k] in AirplaneLabels):
                     if output_prob[top_inds[0]] > 0.0:
                         print 'Shown class is:', top_inds[k]
-                        print 'output label:', labels[top_inds[k]]    
+                        print 'output label:', labels[top_inds[k]]
                         print 'prob', output_prob[top_inds[k]]
-                        Position.append((x,y))
+                        Position.append((x, y))
 #                        carNum = carNum + 1
                    #     break
         # Draw rectangles around each airplane
@@ -117,10 +118,11 @@
         for pos in Position:
             xpos = pos[0]
             ypos = pos[1]
-            cv2.rectangle(frame,(xpos-patch_size,ypos-patch_size),(xpos+patch_size,ypos+patch_size),(0,255,0),2)
+            cv2.rectangle(frame, (xpos-patch_size, ypos-patch_size),
+                          (xpos+patch_size, ypos+patch_size), (0, 255, 0), 2)
            # break
         # out.write(frame)
-        cv2.imshow('frame',frame)
+        cv2.imshow('frame', frame)
         cv2.waitKey()
     # Show image frame on screen
     count = count + 1
@@ -133,6 +135,6 @@
     if count > cap.get(7)/2:
         break
 '''
-#out.release()
+# out.release()
 cap.release()
 cv2.destroyAllWindows()