Added comments, corrected function and variable names and function types.

.vscode/.ropeproject/config.py

+# The default ``config.py``
+# flake8: noqa
+
+
+def set_prefs(prefs):
+    """This function is called before opening the project"""
+
+    # Specify which files and folders to ignore in the project.
+    # Changes to ignored resources are not added to the history and
+    # VCSs.  Also they are not returned in `Project.get_files()`.
+    # Note that ``?`` and ``*`` match all characters but slashes.
+    # '*.pyc': matches 'test.pyc' and 'pkg/test.pyc'
+    # 'mod*.pyc': matches 'test/mod1.pyc' but not 'mod/1.pyc'
+    # '.svn': matches 'pkg/.svn' and all of its children
+    # 'build/*.o': matches 'build/lib.o' but not 'build/sub/lib.o'
+    # 'build//*.o': matches 'build/lib.o' and 'build/sub/lib.o'
+    prefs['ignored_resources'] = ['*.pyc', '*~', '.ropeproject',
+                                  '.hg', '.svn', '_svn', '.git', '.tox']
+
+    # Specifies which files should be considered python files.  It is
+    # useful when you have scripts inside your project.  Only files
+    # ending with ``.py`` are considered to be python files by
+    # default.
+    # prefs['python_files'] = ['*.py']
+
+    # Custom source folders:  By default rope searches the project
+    # for finding source folders (folders that should be searched
+    # for finding modules).  You can add paths to that list.  Note
+    # that rope guesses project source folders correctly most of the
+    # time; use this if you have any problems.
+    # The folders should be relative to project root and use '/' for
+    # separating folders regardless of the platform rope is running on.
+    # 'src/my_source_folder' for instance.
+    # prefs.add('source_folders', 'src')
+
+    # You can extend python path for looking up modules
+    # prefs.add('python_path', '~/python/')
+
+    # Should rope save object information or not.
+    prefs['save_objectdb'] = True
+    prefs['compress_objectdb'] = False
+
+    # If `True`, rope analyzes each module when it is being saved.
+    prefs['automatic_soa'] = True
+    # The depth of calls to follow in static object analysis
+    prefs['soa_followed_calls'] = 0
+
+    # If `False` when running modules or unit tests "dynamic object
+    # analysis" is turned off.  This makes them much faster.
+    prefs['perform_doa'] = True
+
+    # Rope can check the validity of its object DB when running.
+    prefs['validate_objectdb'] = True
+
+    # How many undos to hold?
+    prefs['max_history_items'] = 32
+
+    # Shows whether to save history across sessions.
+    prefs['save_history'] = True
+    prefs['compress_history'] = False
+
+    # Set the number spaces used for indenting.  According to
+    # :PEP:`8`, it is best to use 4 spaces.  Since most of rope's
+    # unit-tests use 4 spaces it is more reliable, too.
+    prefs['indent_size'] = 4
+
+    # Builtin and c-extension modules that are allowed to be imported
+    # and inspected by rope.
+    prefs['extension_modules'] = []
+
+    # Add all standard c-extensions to extension_modules list.
+    prefs['import_dynload_stdmods'] = True
+
+    # If `True` modules with syntax errors are considered to be empty.
+    # The default value is `False`; When `False` syntax errors raise
+    # `rope.base.exceptions.ModuleSyntaxError` exception.
+    prefs['ignore_syntax_errors'] = False
+
+    # If `True`, rope ignores unresolvable imports.  Otherwise, they
+    # appear in the importing namespace.
+    prefs['ignore_bad_imports'] = False
+
+    # If `True`, rope will insert new module imports as
+    # `from <package> import <module>` by default.
+    prefs['prefer_module_from_imports'] = False
+
+    # If `True`, rope will transform a comma list of imports into
+    # multiple separate import statements when organizing
+    # imports.
+    prefs['split_imports'] = False
+
+    # If `True`, rope will remove all top-level import statements and
+    # reinsert them at the top of the module when making changes.
+    prefs['pull_imports_to_top'] = True
+
+    # If `True`, rope will sort imports alphabetically by module name instead
+    # of alphabetically by import statement, with from imports after normal
+    # imports.
+    prefs['sort_imports_alphabetically'] = False
+
+    # Location of implementation of
+    # rope.base.oi.type_hinting.interfaces.ITypeHintingFactory In general
+    # case, you don't have to change this value, unless you're an rope expert.
+    # Change this value to inject you own implementations of interfaces
+    # listed in module rope.base.oi.type_hinting.providers.interfaces
+    # For example, you can add you own providers for Django Models, or disable
+    # the search type-hinting in a class hierarchy, etc.
+    prefs['type_hinting_factory'] = (
+        'rope.base.oi.type_hinting.factory.default_type_hinting_factory')
+
+
+def project_opened(project):
+    """This function is called after opening the project"""
+    # Do whatever you like here!

manual-control-pygame.py → tello_code_hunt_pygame_manual_control.py

-from djitellopy import Tello
+""" This file contains a program to control a DJI Tello drone. 
+    It finds specific QR codes on the drone's video and projects an AR image onto the frames.
+    For QR code recognition it uses Dynamsoft Barcode Reader.
+    To handle frames it uses OpenCV.
+    To control the drone and show the video, it uses pygame.
+"""
+import time
 import cv2
 import pygame
 import numpy as np
-import time
-import os
-import sys
+from djitellopy import Tello
 from dbr import *
 
 # Speed of the drone
@@ -13,7 +17,7 @@ S = 60
 # A low number also results in input lag, as input information is processed once per frame.
 FPS = 120
 
-class FrontEnd(object):
+class FrontEnd():
     """ Maintains the Tello display and moves it through the keyboard keys.
         Press escape key to quit.
         The controls are:
@@ -55,7 +59,7 @@ class FrontEnd(object):
         self.json_file = "settings.json"
         self.reader = BarcodeReader()
 
-        self.arIms = [cv2.imread('minta1.png', cv2.IMREAD_UNCHANGED), cv2.imread('minta2.png', cv2.IMREAD_UNCHANGED), 
+        self.ar_images = [cv2.imread('minta1.png', cv2.IMREAD_UNCHANGED), cv2.imread('minta2.png', cv2.IMREAD_UNCHANGED), 
             cv2.imread('minta3.png', cv2.IMREAD_UNCHANGED), cv2.imread('minta4.png', cv2.IMREAD_UNCHANGED)]
                 
         # Keeps the detected qr code between frames for a while to lessen the flashing effect
@@ -90,22 +94,27 @@ class FrontEnd(object):
         # store AR images for overlay to decrease flashing
         self.warped = []
 
-    # The callback function for receiving barcode results
     def on_barcode_result(self,data):
+        """ The callback function for receiving barcode results."""
         self.qrcodes = data
         self.capture_time = self.get_time
 
-    def get_time(self):
+    @staticmethod
+    def get_time():
+        """ The function for reading current time."""
         localtime = time.localtime()
         capturetime = time.strftime("%Y%m%d%H%M%S", localtime)
         return capturetime
 
     def decorate_frame(self,frame):
+        """ This function adds the warped AR images to the given frame.
+        Arguments:
+            frame: frame to project the warped AR images onto
+        """
         frame = frame.astype(float)
         if self.detection_smooth_counter >= 0:
             for warped in self.warped:
                 background = frame
-                (h,w) = background.shape[:2]
                 foreground = warped[:,:,:3]
                 alpha1 = warped[...,-1]
                 alpha = cv2.merge((alpha1,alpha1,alpha1))
@@ -130,7 +139,10 @@ class FrontEnd(object):
         return frame
 
     def calculate_warped(self):
-        if self.qrcodes != None:
+        """ Calculates transformed images to add to the frames, based on the QR codes.
+            If no QR codes are found or in the queue the list of warped images is deleted.
+        """
+        if self.qrcodes is not None:
             capture_time = self.capture_time
             self.warped = []
             self.detection_smooth_counter = 60
@@ -147,72 +159,91 @@ class FrontEnd(object):
                 points = text_result.localization_result.localization_points
 
                 if text_result.barcode_text == '1':
-                    arIm = self.arIms[0]
+                    ar_image = self.ar_images[0]
                 elif text_result.barcode_text == '2':
-                    arIm = self.arIms[1]
+                    ar_image = self.ar_images[1]
                 elif text_result.barcode_text == '3':
-                    arIm = self.arIms[2]
+                    ar_image = self.ar_images[2]
                 elif text_result.barcode_text == '4':
-                    arIm = self.arIms[3]
+                    ar_image = self.ar_images[3]
                 else:
-                    arIm = np.zeros([1,1,3])
+                    ar_image = np.zeros([1,1,3])
                 # Calculate transform
-                dst = self.order_points(points)
-                convHullSize = np.max(arIm.shape)
+                destination_points = self.order_points(points)
+                convex_hull_size = np.max(ar_image.shape)
                 scale = 4
-                verticalDisplacement = 0
-                horizontalDisplacement = -(1.5*convHullSize/scale)
-                rect = np.array([
-                    [-horizontalDisplacement, -verticalDisplacement],
-                    [-horizontalDisplacement + convHullSize/scale - 1, -verticalDisplacement],
-                    [-horizontalDisplacement + convHullSize/scale - 1, -verticalDisplacement + convHullSize/scale - 1],
-                    [-horizontalDisplacement, -verticalDisplacement + convHullSize/scale - 1]], dtype = "float32")
-                M = cv2.getPerspectiveTransform(rect, dst)
-                self.warped.append(cv2.warpPerspective(arIm, M, (self.im_width, self.im_height)))
+                vertical_displacement = 0
+                horizontal_displacement = -(1.5*convex_hull_size/scale)
+                rectangular = np.array([
+                    [-horizontal_displacement, -vertical_displacement],
+                    [-horizontal_displacement + convex_hull_size/scale - 1, -vertical_displacement],
+                    [-horizontal_displacement + convex_hull_size/scale - 1, -vertical_displacement + convex_hull_size/scale - 1],
+                    [-horizontal_displacement, -vertical_displacement + convex_hull_size/scale - 1]], dtype = "float32")
+                transformation_matrix = cv2.getPerspectiveTransform(rectangular, destination_points)
+                self.warped.append(cv2.warpPerspective(ar_image, transformation_matrix, (self.im_width, self.im_height)))
             if self.capture_time == capture_time:
                 self.qrcodes = None
-        else:
-            self.detection_smooth_counter = self.detection_smooth_counter-1
 
-    def ResizeWithAspectRatio(self,image, width=None, height=None, inter=cv2.INTER_AREA):
-        dim = None
-        (h, w) = image.shape[:2]
+    @staticmethod
+    def resize_with_aspect_ratio( image, width=None, height=None, interpolation_method=cv2.INTER_AREA):
+        """ This function resizes an image based on the given width or height,
+            keeping the aspect ratio. The interpolation method can be specified
+            with the interpolation_method parameter.
+        Arguments:
+            image: image to resize
+            width: image width
+            height: image height
+            interpolation_method: method used for interpolation in OpenCV
+        """
+        dimensions = None
+        (original_height, original_width) = image.shape[:2]
 
         if width is None and height is None:
             return image
         if width is None:
-            r = height / float(h)
-            dim = (int(w * r), height)
+            ratio = height / float(original_height)
+            dimensions = (int(original_width * ratio), height)
         else:
-            r = width / float(w)
-            dim = (width, int(h * r))
-
-        return cv2.resize(image, dim, interpolation=inter)
-
-    def order_points(self,pts):
+            ratio = width / float(original_width)
+            dimensions = (width, int(original_height * ratio))
+
+        return cv2.resize(image, dimensions, interpolation=interpolation_method)
+
+    @staticmethod
+    def order_points(points):
+        """ Orders 2D points of a given rectangular such that
+            the first entry in the list is the top-left,
+            the second entry is the top-right,
+            the third is the bottom-right, and
+            the fourth is the bottom-left.
+        Arguments:
+            points: array or list of 2D points
+        """
         # if points are in a list, construct an array
-        pts = np.array(pts, dtype="float32")
+        points = np.array(points, dtype="float32")
         # initialzie a list of coordinates that will be ordered
         # such that the first entry in the list is the top-left,
         # the second entry is the top-right, the third is the
         # bottom-right, and the fourth is the bottom-left
-        rect = np.zeros((4, 2), dtype = "float32")
+        ordered_points = np.zeros((4, 2), dtype = "float32")
         # the top-left point will have the smallest sum, whereas
         # the bottom-right point will have the largest sum
-        s = pts.sum(axis = 1)
-        rect[0] = pts[np.argmin(s)]
-        rect[2] = pts[np.argmax(s)]
+        sum_ = points.sum(axis = 1)
+        ordered_points[0] = points[np.argmin(sum_)]
+        ordered_points[2] = points[np.argmax(sum_)]
         # now, compute the difference between the points, the
         # top-right point will have the smallest difference,
         # whereas the bottom-left will have the largest difference
-        diff = np.diff(pts, axis = 1)
-        rect[1] = pts[np.argmin(diff)]
-        rect[3] = pts[np.argmax(diff)]
+        diff = np.diff(points, axis = 1)
+        ordered_points[1] = points[np.argmin(diff)]
+        ordered_points[3] = points[np.argmax(diff)]
         # return the ordered coordinates
-        return rect
+        return ordered_points
 
     def run(self):
+        """ Main thread."""
 
+       # Connect to Tello drone and set default speed.
         self.tello.connect()
         self.tello.set_speed(self.speed)
 
@@ -245,10 +276,10 @@ class FrontEnd(object):
 
             frame = frame_read.frame
 
-            frame = self.ResizeWithAspectRatio(frame, height=self.im_height)
+            frame = self.resize_with_aspect_ratio(frame, height=self.im_height)
             try:
-                ret = self.reader.append_video_frame(frame)
-            except:
+                self.reader.append_video_frame(frame)
+            except Exception:
                 pass
 
             self.calculate_warped()
@@ -321,6 +352,7 @@ class FrontEnd(object):
 
 
 def main():
+    """ Main class to create and start the frontend."""
     frontend = FrontEnd()
 
     # run frontend