Sourcery refactored master branch

LaneDetection/CameraCalibration.py

@@ -54,16 +54,14 @@ def calculate_camera_calibration(path_pattern, rows, cols):
 
     ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, CAL_IMAGE_SIZE[:-1], None, None)
 
-    calibration = {'objpoints': objpoints,
+    return {'objpoints': objpoints,
                    'imgpoints': imgpoints,
                    'cal_images': cal_images,
                    'mtx': mtx,
                    'dist': dist,
                    'rvecs': rvecs,
                    'tvecs': tvecs}
 
-    return calibration
-
 
 def get_camera_calibration():
     """

LaneDetection/ImageProcessing.py

@@ -26,10 +26,7 @@
     cam_calibrator = CameraCalibrator(img[:, :, 0].shape[::-1], cam_calibration)
     ld = LaneDetector(SRC, DST, n_frames=FRAME_MEMORY, cam_calibration=cam_calibrator, transform_offset=OFFSET)
 
-    images = []
-    for i in range(1, 12):
-        images.append(imread('../test_images/test%s.jpg' % i))
-
+    images = [imread('../test_images/test%s.jpg' % i) for i in range(1, 12)]
     rows = len(images)
     cols = 2
     fig, axis = plt.subplots(rows, cols)

LaneDetection/ImageUtils.py

@@ -20,9 +20,7 @@ def abs_sobel(img_ch, orient='x', sobel_kernel=3):
         raise ValueError('orient has to be "x" or "y" not "%s"' % orient)
 
     sobel = cv2.Sobel(img_ch, -1, *axis, ksize=sobel_kernel)
-    abs_s = np.absolute(sobel)
-
-    return abs_s
+    return np.absolute(sobel)
 
 
 def gradient_magnitude(sobel_x, sobel_y):
@@ -68,9 +66,7 @@ def extract_yellow(img):
     :return: Yellow 255 not yellow 0
     """
     hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
-    mask = cv2.inRange(hsv, (20, 50, 150), (40, 255, 255))
-
-    return mask
+    return cv2.inRange(hsv, (20, 50, 150), (40, 255, 255))
 
 
 def extract_dark(img):
@@ -80,8 +76,7 @@ def extract_dark(img):
     :return: Dark 255 not dark 0
     """
     hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
-    mask = cv2.inRange(hsv, (0, 0, 0.), (255, 153, 128))
-    return mask
+    return cv2.inRange(hsv, (0, 0, 0.), (255, 153, 128))
 
 
 def extract_highlights(img, p=99.9):
@@ -92,8 +87,7 @@ def extract_highlights(img, p=99.9):
     :return: Highlight 255 not highlight 0
     """
     p = int(np.percentile(img, p) - 30)
-    mask = cv2.inRange(img, p, 255)
-    return mask
+    return cv2.inRange(img, p, 255)
 
 
 def binary_noise_reduction(img, thresh):

LaneDetection/LaneDetector.py

@@ -43,10 +43,9 @@ def __line_plausible(self, left, right):
         """
         if len(left[0]) < 3 or len(right[0]) < 3:
             return False
-        else:
-            new_left = Line(y=left[0], x=left[1])
-            new_right = Line(y=right[0], x=right[1])
-            return are_lanes_plausible(new_left, new_right)
+        new_left = Line(y=left[0], x=left[1])
+        new_right = Line(y=right[0], x=right[1])
+        return are_lanes_plausible(new_left, new_right)
 
     def __check_lines(self, left_x, left_y, right_x, right_y):
         """

LaneDetection/Line.py

@@ -81,9 +81,7 @@ def is_current_fit_parallel(self, other_line, threshold=(0, 0)):
         first_coefi_dif = np.abs(self.current_fit[0] - other_line.current_fit[0])
         second_coefi_dif = np.abs(self.current_fit[1] - other_line.current_fit[1])
 
-        is_parallel = first_coefi_dif < threshold[0] and second_coefi_dif < threshold[1]
-
-        return is_parallel
+        return first_coefi_dif < threshold[0] and second_coefi_dif < threshold[1]
 
     def get_current_fit_distance(self, other_line):
         """
@@ -118,6 +116,6 @@ def calc_curvature(fit_cr):
     y_eval = np.max(y)
 
     fit_cr = np.polyfit(y * ym_per_pix, x * xm_per_pix, 2)
-    curverad = ((1 + (2 * fit_cr[0] * y_eval / 2. + fit_cr[1]) ** 2) ** 1.5) / np.absolute(2 * fit_cr[0])
-
-    return curverad
+    return (
+        (1 + (2 * fit_cr[0] * y_eval / 2.0 + fit_cr[1]) ** 2) ** 1.5
+    ) / np.absolute(2 * fit_cr[0])