"""
Implementation of scene detection algorithms.
"""
import numpy as np
import scipy.ndimage
import scipy.spatial
from ..motion.gme import globalEdgeMotion
from ..utils import *
def _percentage_distance(canny_in, canny_out, r):
diamond = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
E_1 = scipy.ndimage.morphology.binary_dilation(canny_in, structure=diamond, iterations=r)
E_2 = scipy.ndimage.morphology.binary_dilation(canny_out, structure=diamond, iterations=r)
return 1.0 - np.float32(np.sum(E_1 & E_2))/np.float32(np.sum(E_1))
def _scenedet_edges(videodata, threshold, min_scene_len=2):
# the first frame is always a new scene
detected_scenes = [0]
r = 6
# grayscale
luminancedata = rgb2gray(videodata)
numFrames, height, width, channels = luminancedata.shape
# lop off the meaningless dimension
luminancedata = luminancedata[:, :, :, 0]
for t in range(0, numFrames-1):
canny_in = canny(luminancedata[t])
canny_out = canny(luminancedata[t+1])
# estimate the motion
disp = globalEdgeMotion(canny_in, canny_out)
canny_out = np.roll(canny_out, disp[0], axis=0)
canny_out = np.roll(canny_out, disp[1], axis=1)
# compute percentage
p_in = _percentage_distance(canny_in, canny_out, r)
p_out = _percentage_distance(canny_out, canny_in, r)
# print "percentage: ", bt - at
p = np.max((p_in, p_out))
if (p > threshold) and (t - detected_scenes[len(detected_scenes)-1] > min_scene_len):
detected_scenes.append(t+1)
return np.array(detected_scenes)
def _scenedet_histogram(videodata, parameter1, min_scene_len=2):
# the first frame is always a new scene
detected_scenes = [0]
# grayscale
numFrames, height, width, channels = videodata.shape
for t in range(0, numFrames-1):
curr = rgb2gray(videodata[t])
nxt = rgb2gray(videodata[t+1])
curr = curr[0, :, :, 0]
nxt = nxt[0, :, :, 0]
hist1, bins = np.histogram(curr, bins=256, range=(0, 255))
hist2, bins = np.histogram(nxt, bins=256, range=(0, 255))
hist1 = hist1.astype(np.float32)
hist2 = hist2.astype(np.float32)
hist1 /= 256.0
hist2 /= 256.0
framediff = np.mean(np.abs(hist1 - hist2))
if (framediff > parameter1) and (t - detected_scenes[len(detected_scenes)-1] > min_scene_len):
detected_scenes.append(t+1)
return np.array(detected_scenes)
def _scenedet_intensity(videodata, parameter1, min_scene_len=2, colorspace='hsv'):
detected_scenes = [0]
numFrames, height, width, channels = videodata.shape
for t in range(0, numFrames-1):
frame0 = videodata[t].astype(np.float32)
frame1 = videodata[t+1].astype(np.float32)
delta = np.sum(np.abs(frame1 - frame0)/(height * width * channels))
if (delta > parameter1) and (t - detected_scenes[len(detected_scenes)-1] > min_scene_len):
detected_scenes.append(t+1)
return np.array(detected_scenes)
[docs]def scenedet(videodata, method='histogram', parameter1=None, min_scene_length=2):
"""Scene detection algorithms
Given a sequence of frames, this function
is able to run find the first index of new
scenes.
Parameters
----------
videodata : ndarray
an input frame sequence, shape (T, M, N, C), (T, M, N), (M, N, C) or (M, N)
method : string
"histogram" --> threshold-based (parameter1 defaults to 1.0) approach using intensity histogram differences. [#f1]_
"edges" --> threshold-based (parameter1 defaults to 0.5) approach measuring the edge-change fraction after global motion compensation [#f2]_
"intensity" --> Detects fast cuts using changes in colour and intensity between frames. Parameter1 is the threshold used for detection, which defaults to 30.0.
parameter1 : float
Number used as a tuning parameter. See method argument for details.
min_scene_length : int
Number used for determining minimum scene length.
Returns
----------
sceneCuts : ndarray, shape (numScenes,)
The indices corresponding to the first frame in the detected scenes.
References
----------
.. [#f1] Kiyotaka Otsuji and Yoshinobu Tonomura. Projection-detecting filter for video cut detection. Multimedia Systems 1.5, 205-210, 1994.
.. [#f2] Kevin Mai, Ramin Zabih, and Justin Miller. Feature-based algorithms for detecting and classifying scene breaks. Cornell University, 1995.
"""
videodata = vshape(videodata)
detected_scenes = []
if method == "histogram":
if parameter1 is None:
parameter1 = 1.0
detected_scenes = _scenedet_histogram(videodata, parameter1, min_scene_length)
elif method == "edges":
if parameter1 is None:
parameter1 = 0.6
detected_scenes = _scenedet_edges(videodata, parameter1, min_scene_length)
elif method == "intensity":
if parameter1 is None:
parameter1 = 1.0
detected_scenes = _scenedet_intensity(videodata, parameter1, min_scene_length)
else:
raise NotImplementedError
return detected_scenes
