Nitpicky fixes in examples

Author: sfe-SparkFro

examples/ex01_hello_opencv.py

@@ -17,7 +17,7 @@
 # Standard OpenCV leverages the host operating system to access hardware, but we
 # don't have that luxury in MicroPython. Instead, drivers are provided for
 # various hardware components, which need to be initialized before using them.
-# The exmples import a module called `cv2_hardware_init`, which initializes the
+# The examples import a module called `cv2_hardware_init`, which initializes the
 # drivers. You may need to edit the contents of the `cv2_hardware_init` module
 # based on your specific board and hardware configuration
 from cv2_hardware_init import *
@@ -61,7 +61,7 @@
 cv.imshow(display, img) # Can alternatively call `display.imshow(img)`
 
 # Standard OpenCV requires a call to `cv.waitKey()` to process events and
-# actually display the image. However the display driver shows the image
+# actually display the image. However, the display driver shows the image
 # immediately, so it's not necessary to call `cv.waitKey()` in MicroPython.
 # But it is available, and behaves almost like any other Python environment! The
 # only difference is that it requires a key to be pressed in the REPL instead of

examples/ex02_camera.py

@@ -34,7 +34,7 @@
     success, frame = camera.read()
 
     # Check if the frame was read successfully
-    if success == False:
+    if not success:
         print("Error reading frame from camera")
         break
 

examples/ex03_touch_screen.py

@@ -52,7 +52,7 @@
     else:
         # Check if there was touch input before
         if touch_input:
-            # There was touch input before, but not any more
+            # There was touch input before, but not anymore
             touch_input = False
 
     # Draw a line if there was touch input

examples/ex05_performance.py

@@ -56,7 +56,7 @@
 # Loop to continuously read frames from the camera and display them
 while True:
     # Read a frame from the camera and measure how long it takes. Try running
-    # this both with and without the preallocated `frame` array to see the
+    # this both with and without the pre-allocated `frame` array to see the
     # difference in performance
     t0 = time.ticks_us()
     success, frame = camera.read(frame)
@@ -69,7 +69,7 @@
         break
 
     # Now we'll do some processing on the frame. Try running this with and
-    # without the preallocated `result_image` array, and try different OpenCV
+    # without the pre-allocated `result_image` array, and try different OpenCV
     # functions to compare performance
     t0 = time.ticks_us()
     result_image = cv.cvtColor(frame, cv.COLOR_BGR2HSV, result_image)
@@ -110,21 +110,20 @@
     # since it mitigates how frequently garbage collection is triggered
     if memory_used < 0:
         print("Garbage collection triggered!")
-    
+
     # Something to try is triggering the garbage collector manually each loop
     # iteration to immediately free up memory. Garbage collection can be faster
     # if less memory has been allocated, so this can help avoid long stutters
-    # from occasional garbage collection. However garbage collection will always
-    # take *some* time, so this will lower the average FPS. You can choose to do
-    # this if you prefer a consistent frame rate, or don't if you prefer maximum
-    # frame rate and are okay with occasional stutters
-    # gc.collect()
+    # from occasional garbage collection. However, garbage collection always
+    # takes *some* time, so this will lower the average FPS. You can choose to
+    # do this if you prefer a consistent frame rate, or don't if you prefer
+    # maximum frame rate and are okay with occasional stutters gc.collect()
 
     # For advanced users, you can use the internal buffers of the camera and
     # display drivers: `camera._buffer` and `display._buffer`. Using these
     # buffers directly can avoid the colorspace conversions implemented in
     # `camera.read()` and `display.imshow()`, which can improve performance if
-    # your application can make use of the native colorspaces and improve
+    # your application can make use of the native color spaces and improve
     # overall performance
 
     # Check for key presses

examples/xrp_examples/ex02_grab_orange_ring.py

@@ -55,20 +55,20 @@ def find_orange_ring_pipeline(frame):
     # Value: Anything above 30 is bright enough
     lower_bound = (15, 50, 30)
     upper_bound = (25, 255, 255)
-    inRange = cv.inRange(hsv, lower_bound, upper_bound)
+    in_range = cv.inRange(hsv, lower_bound, upper_bound)
 
     # Noise in the image often causes `cv.inRange()` to return false positives
     # and false negatives, meaning there are some incorrect pixels in the binary
     # image. These can be cleaned up with morphological operations, which
     # effectively grow and shrink regions in the binary image to remove tiny
     # blobs of noise
     kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
-    morphOpen = cv.morphologyEx(inRange, cv.MORPH_OPEN, kernel)
-    morphClose = cv.morphologyEx(morphOpen, cv.MORPH_CLOSE, kernel)
+    morph_open = cv.morphologyEx(in_range, cv.MORPH_OPEN, kernel)
+    morph_close = cv.morphologyEx(morph_open, cv.MORPH_CLOSE, kernel)
 
     # Now we use `cv.findContours()` to find the contours in the binary image,
     # which are the boundaries of the regions in the binary image
-    contours, hierarchy = cv.findContours(morphClose, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contours, hierarchy = cv.findContours(morph_close, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
 
     # It's possible that no contours were found, so first check if any were
     # found before proceeding
@@ -91,7 +91,7 @@ def find_orange_ring_pipeline(frame):
 
     # If no contour was found, return invalid values to indicate that
     if best_contour is None:
-        return (-1, -1)
+        return -1, -1
 
     # Calculate the bounding rectangle of the contour, and use that to calculate
     # the center coordinates of the ring
@@ -141,7 +141,7 @@ def find_orange_ring_pipeline(frame):
 
     # Now we can return the distance and position of the ring in cm, since
     # that's the only data we need from this pipeline
-    return (distance_cm, position_x_cm)
+    return distance_cm, position_x_cm
 
 # Move the servo out of the way of the camera
 servo_one.set_angle(90)
@@ -157,7 +157,7 @@ def find_orange_ring_pipeline(frame):
 while True:
     # Read a frame from the camera
     success, frame = camera.read()
-    if success == False:
+    if not success:
         print("Error reading frame from camera")
         break