import cv2

video = cv2.VideoCapture()
video.open("videos/testvideo.mp4")
width  = video.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
height = video.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
nframes = video.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)
print width,height,nframes

def frames(fname):
    video = cv2.VideoCapture()
    assert video.open(fname)
    while 1:
        ok,image = video.read()
        if not ok: break
        yield image
    video.release()

for i,image in enumerate(frames("videos/testvideo.mp4")):
    if i==100: break
imshow(image)

from itertools import islice
imshow(next(islice(frames("videos/testvideo.mp4"),100,101)))

means = [mean(image) for image in frames("videos/testvideo.mp4")]
plot(means)


from collections import deque

# compute deltas between two frames
deltas = []; nbuf = 3
buffer = deque()
for image in islice(frames("videos/quantum.mp4"),0,2000):
    buffer.append(image)
    while len(buffer)>nbuf: buffer.popleft()
    deltas.append(mean(abs(buffer[-1]-buffer[0])))
deltas = array(deltas); plot(deltas)

from scipy.ndimage import filters
usm = deltas-filters.gaussian_filter(deltas,2.0)
plot(usm)

usm = where(usm<0.2*amax(usm),0,usm)
plot(usm)

usm = where(usm<filters.maximum_filter(usm,240),0,usm)
plot(usm)

find(usm)

locs = find(usm)
keyindexes = [int(mean([a,b])) for a,b in zip([0]+list(locs),locs)]
print keyindexes


figsize(12,6)

# keyframes
keyframes = []
for i,image in enumerate(frames("videos/quantum.mp4")):
    if i in keyindexes: keyframes.append(image)
for i,image in enumerate(keyframes[:8]):
    subplot(2,4,i+1); axis("off"); imshow(image)


figsize(9,6)
from scipy.ndimage import interpolation

samples = []
for image in islice(frames("videos/quantum.mp4"),0,3000,15):
    samples.append(interpolation.zoom(image,[0.25,0.25,1]))
samples = array(samples,'f')*1.0/amax(samples)
for i,s in enumerate(samples[16:32]):
    subplot(4,4,i+1); axis("off"); imshow(s)

from sklearn.cluster import KMeans
km = KMeans(n_clusters=25)
data = samples.reshape(len(samples),-1)
km.fit(data)

pred = km.predict(data)
plot(pred)

from collections import Counter
counts = Counter(pred)
counts = [counts[i] for i in range(amax(counts.keys()))]
print sorted(counts)[::-1]
order = argsort(counts)[::-1]

for f,i in enumerate(order):
    c = km.cluster_centers_[i]; subplot(5,5,f+1); axis("off"); imshow(c.reshape(fshape))

n = 2900
fpair = array(list(islice(frames("videos/quantum.mp4"),n,n+8,4)))/255.0
fpair = mean(fpair,axis=3)
subplot(121); imshow(fpair[0],cmap=cm.gray)
subplot(122); imshow(abs(fpair[1]-fpair[0]))


rows,cols = fpair[0].shape

flow = cv2.calcOpticalFlowFarneback(fpair[0],fpair[1],0.5,3,15,3,5,1.2,0)
flow /= amax(abs(flow))
subplot(121); imshow(flow[:,:,0])
subplot(122); imshow(flow[:,:,1])ß

# optical flow overlaid over original image
rgb = transpose(array([flow[:,:,0],flow[:,:,1],0.5*fpair[1]/amax(fpair[1])]),[1,2,0])
imshow(abs(rgb)/amax(rgb))