rename as previous example now video also

src/blur_video.py

@@ -0,0 +1,115 @@
+#####################################################################
+
+# Task 1 : capture live video from an attached camera
+
+#####################################################################
+
+import cv2
+import math
+
+#####################################################################
+
+# this function is called as a call-back everytime the trackbar is moved
+# (here we just do nothing)
+
+
+def nothing(x):
+    pass
+
+
+#####################################################################
+
+# define video capture with access to camera 0
+
+camera = cv2.VideoCapture(0)
+
+# define display window
+
+window_name = "Live Camera Input with Blurring"
+cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)
+
+# add some track bar GUI controllers for smoothing filter parameters
+
+smoothing_neighbourhood_x = 3
+cv2.createTrackbar(
+    "filter size - X",
+    window_name,
+    smoothing_neighbourhood_x,
+    250,
+    nothing)
+
+smoothing_neighbourhood_y = 3
+cv2.createTrackbar(
+    "filter size - Y",
+    window_name,
+    smoothing_neighbourhood_y,
+    250,
+    nothing)
+
+#####################################################################
+
+keep_processing = True
+
+while (keep_processing):
+
+    # read an image from the camera
+
+    _, image = camera.read()
+
+    # start a timer (to see how long processing and display takes)
+
+    start_t = cv2.getTickCount()
+
+    # get parameters from track bars
+
+    smoothing_neighbourhood_x = cv2.getTrackbarPos("filter size - X",
+                                                   window_name)
+    smoothing_neighbourhood_y = cv2.getTrackbarPos("filter size - Y",
+                                                   window_name)
+
+    # check filter sizes are greater than 3 and odd
+
+    smoothing_neighbourhood_x = max(3, smoothing_neighbourhood_x)
+    if not (smoothing_neighbourhood_x % 2):
+        smoothing_neighbourhood_x = smoothing_neighbourhood_x + 1
+
+    smoothing_neighbourhood_y = max(3, smoothing_neighbourhood_y)
+    if not (smoothing_neighbourhood_y % 2):
+        smoothing_neighbourhood_y = smoothing_neighbourhood_y + 1
+
+    # performing smoothing on the image using a smoothing filter
+
+    smoothed_image = cv2.GaussianBlur(image, (smoothing_neighbourhood_x,
+                                      smoothing_neighbourhood_y), 0)
+
+    # stop the timer and convert to milliseconds
+    # (to see how long processing and display takes)
+
+    stop_t = ((cv2.getTickCount() - start_t) /
+              cv2.getTickFrequency()) * 1000
+
+    label = ('Processing time: %.2f ms' % stop_t) + \
+        (' (Max Frames per Second (fps): %.2f' % (1000 / stop_t)) + ')'
+    cv2.putText(smoothed_image, label, (0, 15),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
+
+    # display image
+
+    cv2.imshow(window_name, smoothed_image)
+
+    # start the event loop - if user presses "x" then exit
+
+    # wait 40ms or less for a key press from the user
+    # depending on processing time taken (i.e. 1000ms / 25 fps = 40 ms)
+
+    key = cv2.waitKey(max(2, 40 - int(math.ceil(stop_t)))) & 0xFF
+
+    if (key == ord('x')):
+        keep_processing = False
+
+#####################################################################
+
+# Author : Toby Breckon, [email protected]
+# Copyright (c) 2022 Dept Computer Science, Durham University, UK
+
+#####################################################################