Added existing Part 1 code and test video.

Author: Breakthrough

examples/part1-threshold.py

@@ -0,0 +1,109 @@
+# 
+#      Scene Detection with Python and OpenCV - Example Program
+# Part 1: Threshold/Fade-to-Black Detection      By: Brandon Castellano
+#
+# http://www.bcastell.com/tech-articles/pyscenedetect-tutorial-part-1/
+#
+# This Python program implements a simple threshold-based scene detection
+# algorithm using a set threshold compared to the average pixel intensity
+# of each frame. Usage:
+#
+#   > python part1-threshold.py [video-file] [intensity = 16]
+#
+# Where [video-file] is a path to the video to be parsed, and [intensity]
+# is the average pixel intensity from 0 to 255 to be used as a cut-off
+# (if unspecified, the default value of 16 is used).  Example:
+#
+#   > python part1-threshold.py testvideo.mp4 8
+#
+# For each fade/cut that is detected, the timecodes and frame numbers
+# are printed to stdout. Note that this program depends on the Python
+# OpenCV bindings and NumPy.
+#
+# Copyright (C) 2013-2014 Brandon Castellano <http://www.bcastell.com>.
+# I hereby release this file into the public domain.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+#
+
+
+import sys
+import cv2
+import numpy as np
+
+def main():
+    if len(sys.argv) < 2:
+        print "Error - file name must be specified as first argument."
+        return
+    
+    cap = cv2.VideoCapture()
+    cap.open(sys.argv[1])
+    
+    if not cap.isOpened():
+        print "Fatal error - could not open video %s." % sys.argv[1]
+        return
+    else:
+        print "Parsing video %s..." % sys.argv[1]
+        
+    # Do stuff with cap here.
+
+    width  = cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
+    height = cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
+    print "Video Resolution: %d x %d" % (width, height)
+
+    # Allow the threshold to be passed as an optional, second argument to the script.
+    threshold = 16
+    if len(sys.argv) > 2 and int(sys.argv[2]) > 0:
+        threshold = int(sys.argv[2])
+    print "Detecting scenes with threshold = %d.\n" % threshold
+
+
+    last_mean  = 0                   # Mean intensity of the *last* frame processed.
+    start_time = cv2.getTickCount()  # Used for benchmarking/statistics after loop.
+
+    while True:
+        (rv, im) = cap.read()   # im is a valid image if and only if rv is true
+        if not rv:
+            break
+
+        # im.mean() and numpy.mean(im) run at roughly the same speed
+        #frame_mean = im.mean()
+        #frame_mean = np.mean(im)
+
+        # dividing np.sum(im) by the image size increases speed by ~35-40%
+        frame_mean = np.sum(im) / float(im.shape[0] * im.shape[1] * im.shape[2])
+
+        # Detect fade in from black.
+        if frame_mean >= threshold and last_mean < threshold:
+            print "Detected fade in at %dms (frame %d)." % (
+                cap.get(cv2.cv.CV_CAP_PROP_POS_MSEC),
+                cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES) )
+
+        # Detect fade out to black.
+        elif frame_mean < threshold and last_mean >= threshold:
+            print "Detected fade out at %dms (frame %d)." % (
+                cap.get(cv2.cv.CV_CAP_PROP_POS_MSEC),
+                cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES) )
+
+        last_mean = frame_mean      # Store current mean to compare in next iteration.
+
+    # get # of frames in video as position in video we ended at
+    frame_count = cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)
+    # compute runtime and average framerate
+    total_runtime = float(cv2.getTickCount() - start_time) / cv2.getTickFrequency()
+    avg_framerate = float(frame_count) / total_runtime
+
+    print "Read %d frames from video in %4.2f seconds (avg. %4.1f FPS)." % (
+        frame_count, total_runtime, avg_framerate)
+
+    cap.release()
+
+    
+if __name__ == "__main__":
+    main()