In [6]:

    

import subprocess
import os

not_small_video_files = [f.lower().split(".mp4")[0] for f in os.listdir("videos/") if f[0] != "." and "small" not in f]
small_video_files = [f.lower().split("-small.mp4")[0] for f in os.listdir("videos/") if f[0] != "." and "small" in f]
videos_to_process = [n for n in not_small_video_files if n not in small_video_files]

print not_small_video_files
print small_video_files
print videos_to_process

for video in videos_to_process: 
    print "processing video", video
    fn_in = "videos/" + video + ".mp4"
    fn_out = "videos/" + video + "-small.mp4"
    
    # ffmpeg -i nocuts.MP4 -vf scale=500:-1 nocuts-small.MP4
    # subprocess.call(["ffmpeg", "-i", fn_in, "-vf", "scale=500:-1", fn_out])


    

    




['arctic', 'congo_2048', 'equation-2', 'equation', 'gittes-2', 'gittes-reg-2', 'gittes-reg', 'invasion', 'nocuts', 'probation-reg', 'probation-shoulder', 'steve-walking', 'toy-story-shoulder-2', 'toy-story-shoulder', 'toy-story']
['arctic', 'congo_2048', 'equation-2', 'equation', 'gittes-2', 'gittes-reg-2', 'gittes-reg', 'invasion', 'nocuts', 'nocuts-smaller.mp4', 'probation-reg', 'probation-shoulder', 'steve-walking', 'toy-story-shoulder-2', 'toy-story-shoulder', 'toy-story']
[]

In [7]:

    

import numpy as np
import cv2
from collections import defaultdict
import json

# attempting optical flow on one 360 video
# sparse features 
# from - http://docs.opencv.org/3.2.0/d7/d8b/tutorial_py_lucas_kanade.html

def save_important_points_fn(fn, recorded_important_points):
    
    out_fn = "analysis/optical-flow/" + fn.lower().split(".mp4")[0].split("/")[-1] + ".json"
    print "Saving to", out_fn
    print recorded_important_points[0:20]

    new_points = []
    for p in recorded_important_points:
        p["movement"] = float(p["movement"])
        p["a"] = [float(p["a"][0]), float(p["a"][1])]
        p["b-a"] = [float(p["b-a"][0]), float(p["b-a"][1])]
        p["b"] = [float(p["b"][0]), float(p["b"][1])]
        new_points.append(p)

    with open("analysis/optical-flow/" + fn.lower().split(".mp4")[0].split("/")[-1] + ".json", 'w') as f:
        json.dump(new_points, f)

def find_important_points_for_fn(fn):
    cap = cv2.VideoCapture(fn)
    fps = cap.get(cv2.CAP_PROP_FPS)

    # params for ShiTomasi corner detection
    feature_params = dict( maxCorners = 100,
                           qualityLevel = 0.3,
                           minDistance = 7,
                           blockSize = 7 )
    lk_params = dict( winSize  = (15,15),
                      maxLevel = 2,
                      criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

    # Create some random colors
    color = np.random.randint(0,255,(100,3))

    # Take first frame and find corners in it
    ret, old_frame = cap.read()
    old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
    p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

    # Create a mask image for drawing purposes
    mask = np.zeros_like(old_frame)

    def dist(x,y):   
        return np.sqrt(np.sum((x-y)**2))

    frame_count = 0
    feature_sums = defaultdict(float)
    feature_counts = defaultdict(float)
    feature_initial_values = defaultdict(list)
    recorded_important_points = []

    while(1):
        frame_count += 1

        ret,frame = cap.read()
        
        if frame != None and frame.any(): 
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
            good_new = p1[st==1]
            good_old = p0[st==1]

            for i,(new,old) in enumerate(zip(good_new,good_old)):

                a,b = new.ravel()
                c,d = old.ravel()
                mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
                frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)

                # added to record important points
                diff = dist(new, old)

                key = str(i)
                feature_sums[key] += diff
                feature_counts[key] += 1.0

                if frame_count % 29 == 0 and frame_count != 0:
                    movement = feature_sums[key]/feature_counts[key]
                    b_minus_a = new-feature_initial_values[key]

                    # if abs(b_minus_a[0]) >= 1.0 or abs(b_minus_a[1]) >= 1.0:
                    point = {
                            "b": new.tolist(), 
                            "a": feature_initial_values[key].tolist(),
                            "b-a": b_minus_a.tolist(),
                            "feature": key,
                            "frame": frame_count,
                            "movement": movement,
                            "time": frame_count/fps
                        }
                    recorded_important_points.append(point)

                    feature_initial_values[key] = new

                if frame_count == 1: 
                    feature_initial_values[key] = new

            img = cv2.add(frame,mask)
            cv2.imshow('frame',img)

            k = cv2.waitKey(30) & 0xff
            if k == 27:
                break

            # Now update the previous frame and previous points
            old_gray = frame_gray.copy()
            p0 = good_new.reshape(-1,1,2)
        else:
            return recorded_important_points
    return recorded_important_points
        
    cv2.waitKey(1)
    cv2.destroyAllWindows()
    cv2.waitKey(1)
    cap.release()


    

In [8]:

    

import time

result_root = "analysis/optical-flow/"
small_video_files = [f for f in os.listdir("videos/") if f[0] != "." and "small" in f][2:]
# result_files = [m.split('.json')[0] + ".mp4" for m in os.listdir(result_root) if m[0] != "."]
result_files = [] # need to reprocess all of the files
videos_to_process = [s for s in small_video_files if s.lower() not in result_files and "invasion" not in s]
print videos_to_process

for video_fn in videos_to_process: 
    fn = "videos/" + video_fn
    print "Processing:", fn
    
    start = time.time()
    recorded_important_points = find_important_points_for_fn(fn)
    save_important_points_fn(fn, recorded_important_points)
    print "Minutes to process:", (time.time() - start)/60.0


    

    




['equation-2-small.mp4', 'equation-small.mp4', 'gittes-2-small.mp4', 'gittes-reg-2-small.mp4', 'gittes-reg-small.mp4', 'nocuts-small.MP4', 'nocuts-smaller.mp4', 'probation-reg-small.mp4', 'probation-shoulder-small.mp4', 'steve-walking-small.mp4', 'toy-story-shoulder-2-small.mp4', 'toy-story-shoulder-small.mp4', 'toy-story-small.mp4']
Processing: videos/equation-2-small.mp4
Saving to analysis/optical-flow/equation-2-small.json
[{'a': [215.98696899414062, 127.0182113647461], 'b': [216.0643768310547, 126.94255828857422], 'frame': 29, 'feature': '0', 'b-a': [0.0774078369140625, -0.075653076171875], 'time': 0.9860000000000001, 'movement': 0.019925923212652158}, {'a': [215.96563720703125, 150.0102081298828], 'b': [216.0249481201172, 149.9840087890625], 'frame': 29, 'feature': '1', 'b-a': [0.0593109130859375, -0.0261993408203125], 'time': 0.9860000000000001, 'movement': 0.027426232262675106}, {'a': [453.9976806640625, 138.0035400390625], 'b': [453.99493408203125, 138.00169372558594], 'frame': 29, 'feature': '2', 'b-a': [-0.00274658203125, -0.0018463134765625], 'time': 0.9860000000000001, 'movement': 0.0026466182156080573}, {'a': [447.001708984375, 150.0011749267578], 'b': [447.0017395019531, 150.0034942626953], 'frame': 29, 'feature': '3', 'b-a': [3.0517578125e-05, 0.0023193359375], 'time': 0.9860000000000001, 'movement': 0.0028823505211942667}, {'a': [149.0017852783203, 132.99911499023438], 'b': [149.00064086914062, 133.00009155273438], 'frame': 29, 'feature': '4', 'b-a': [-0.0011444091796875, 0.0009765625], 'time': 0.9860000000000001, 'movement': 0.0030273220317181327}, {'a': [217.0023956298828, 100.99722290039062], 'b': [217.00083923339844, 100.9973373413086], 'frame': 29, 'feature': '5', 'b-a': [-0.001556396484375, 0.00011444091796875], 'time': 0.9860000000000001, 'movement': 0.0041309782049963892}, {'a': [465.99688720703125, 176.0015106201172], 'b': [465.9945373535156, 176.0008544921875], 'frame': 29, 'feature': '6', 'b-a': [-0.002349853515625, -0.0006561279296875], 'time': 0.9860000000000001, 'movement': 0.0029984441814655117}, {'a': [432.0008544921875, 129.99778747558594], 'b': [432.0025329589844, 129.9947509765625], 'frame': 29, 'feature': '7', 'b-a': [0.001678466796875, -0.0030364990234375], 'time': 0.9860000000000001, 'movement': 0.0025254847388513834}, {'a': [150.00193786621094, 160.00018310546875], 'b': [150.00221252441406, 160.00503540039062], 'frame': 29, 'feature': '8', 'b-a': [0.000274658203125, 0.004852294921875], 'time': 0.9860000000000001, 'movement': 0.0035912504074452766}, {'a': [142.99803161621094, 90.9976806640625], 'b': [142.99818420410156, 91.00178527832031], 'frame': 29, 'feature': '9', 'b-a': [0.000152587890625, 0.0041046142578125], 'time': 0.9860000000000001, 'movement': 0.0025682651085562297}, {'a': [480.0015563964844, 182.99473571777344], 'b': [480.0016174316406, 182.99905395507812], 'frame': 29, 'feature': '10', 'b-a': [6.103515625e-05, 0.0043182373046875], 'time': 0.9860000000000001, 'movement': 0.0040286050583557068}, {'a': [143.9955291748047, 147.99830627441406], 'b': [143.99851989746094, 148.00045776367188], 'frame': 29, 'feature': '11', 'b-a': [0.00299072265625, 0.0021514892578125], 'time': 0.9860000000000001, 'movement': 0.0023596675942073866}, {'a': [458.9985656738281, 165.9968719482422], 'b': [458.99530029296875, 165.99940490722656], 'frame': 29, 'feature': '12', 'b-a': [-0.003265380859375, 0.002532958984375], 'time': 0.9860000000000001, 'movement': 0.0028684483118483731}, {'a': [151.99781799316406, 91.00263977050781], 'b': [151.99781799316406, 91.00065612792969], 'frame': 29, 'feature': '13', 'b-a': [0.0, -0.001983642578125], 'time': 0.9860000000000001, 'movement': 0.0028863716730045089}, {'a': [20.998428344726562, 207.00067138671875], 'b': [21.00149917602539, 207.00421142578125], 'frame': 29, 'feature': '14', 'b-a': [0.003070831298828125, 0.0035400390625], 'time': 0.9860000000000001, 'movement': 0.003049156495072911}, {'a': [174.999755859375, 146.9996795654297], 'b': [175.00071716308594, 147.0016632080078], 'frame': 29, 'feature': '15', 'b-a': [0.0009613037109375, 0.001983642578125], 'time': 0.9860000000000001, 'movement': 0.0029302163095743365}, {'a': [231.9958038330078, 133.00108337402344], 'b': [232.00030517578125, 133.0072021484375], 'frame': 29, 'feature': '16', 'b-a': [0.0045013427734375, 0.0061187744140625], 'time': 0.9860000000000001, 'movement': 0.0035297100929992981}, {'a': [17.999435424804688, 220.99977111816406], 'b': [17.970130920410156, 220.99176025390625], 'frame': 29, 'feature': '17', 'b-a': [-0.02930450439453125, -0.0080108642578125], 'time': 0.9860000000000001, 'movement': 0.0031427808501356251}, {'a': [170.00082397460938, 136.00030517578125], 'b': [170.00091552734375, 136.0008544921875], 'frame': 29, 'feature': '18', 'b-a': [9.1552734375e-05, 0.00054931640625], 'time': 0.9860000000000001, 'movement': 0.0033608581288717687}, {'a': [482.9993896484375, 219.9993438720703], 'b': [482.998779296875, 219.9990997314453], 'frame': 29, 'feature': '19', 'b-a': [-0.0006103515625, -0.000244140625], 'time': 0.9860000000000001, 'movement': 0.0028208595999628947}]
Minutes to process: 6.30963134766
Processing: videos/equation-small.mp4
Saving to analysis/optical-flow/equation-small.json
[{'a': [462.002197265625, 221.00393676757812], 'b': [458.6006164550781, 220.28172302246094], 'frame': 29, 'feature': '0', 'b-a': [-3.401580810546875, -0.7222137451171875], 'time': 0.9860000000000001, 'movement': 0.13158754337627185}, {'a': [432.0022888183594, 130.99984741210938], 'b': [431.82830810546875, 130.93592834472656], 'frame': 29, 'feature': '1', 'b-a': [-0.173980712890625, -0.0639190673828125], 'time': 0.9860000000000001, 'movement': 0.0088886008417265946}, {'a': [149.0001220703125, 132.99984741210938], 'b': [149.00003051757812, 133.00100708007812], 'frame': 29, 'feature': '2', 'b-a': [-9.1552734375e-05, 0.00115966796875], 'time': 0.9860000000000001, 'movement': 0.002327817502724617}, {'a': [150.00228881835938, 160.00131225585938], 'b': [150.0072021484375, 159.99351501464844], 'frame': 29, 'feature': '3', 'b-a': [0.004913330078125, -0.0077972412109375], 'time': 0.9860000000000001, 'movement': 0.002321084391261483}, {'a': [216.99948120117188, 101.00432586669922], 'b': [216.99671936035156, 100.9459457397461], 'frame': 29, 'feature': '4', 'b-a': [-0.0027618408203125, -0.058380126953125], 'time': 0.9860000000000001, 'movement': 0.0053480722487839905}, {'a': [143.99777221679688, 147.99771118164062], 'b': [144.0144805908203, 148.002685546875], 'frame': 29, 'feature': '5', 'b-a': [0.0167083740234375, 0.004974365234375], 'time': 0.9860000000000001, 'movement': 0.0035747636786003694}, {'a': [16.99756622314453, 220.9965057373047], 'b': [16.98591423034668, 221.1026153564453], 'frame': 29, 'feature': '6', 'b-a': [-0.011651992797851562, 0.106109619140625], 'time': 0.9860000000000001, 'movement': 0.0060365574297912674}, {'a': [143.9998016357422, 91.00303649902344], 'b': [143.99253845214844, 91.00690460205078], 'frame': 29, 'feature': '7', 'b-a': [-0.00726318359375, 0.00386810302734375], 'time': 0.9860000000000001, 'movement': 0.0032445379151011303}, {'a': [487.1059875488281, 185.0153045654297], 'b': [475.3122863769531, 186.43858337402344], 'frame': 29, 'feature': '8', 'b-a': [-11.793701171875, 1.42327880859375], 'time': 0.9860000000000001, 'movement': 0.59260717249507533}, {'a': [279.9970703125, 181.99595642089844], 'b': [280.0120849609375, 181.98980712890625], 'frame': 29, 'feature': '9', 'b-a': [0.0150146484375, -0.0061492919921875], 'time': 0.9860000000000001, 'movement': 0.0033577080747787037}, {'a': [20.996498107910156, 206.99862670898438], 'b': [21.014408111572266, 207.0485382080078], 'frame': 29, 'feature': '10', 'b-a': [0.017910003662109375, 0.0499114990234375], 'time': 0.9860000000000001, 'movement': 0.0051160250722173733}, {'a': [232.00013732910156, 133.99905395507812], 'b': [231.97787475585938, 133.9452667236328], 'frame': 29, 'feature': '11', 'b-a': [-0.0222625732421875, -0.0537872314453125], 'time': 0.9860000000000001, 'movement': 0.0070421761714708833}, {'a': [175.00039672851562, 146.99412536621094], 'b': [174.9959716796875, 146.96923828125], 'frame': 29, 'feature': '12', 'b-a': [-0.004425048828125, -0.0248870849609375], 'time': 0.9860000000000001, 'movement': 0.0045376200076385305}, {'a': [220.99769592285156, 134.99798583984375], 'b': [220.9851531982422, 134.9285125732422], 'frame': 29, 'feature': '13', 'b-a': [-0.012542724609375, -0.0694732666015625], 'time': 0.9860000000000001, 'movement': 0.0044499510871085883}, {'a': [170.00218200683594, 136.00575256347656], 'b': [170.00216674804688, 136.00416564941406], 'frame': 29, 'feature': '14', 'b-a': [-1.52587890625e-05, -0.0015869140625], 'time': 0.9860000000000001, 'movement': 0.0037544798246456376}, {'a': [151.99990844726562, 90.99927520751953], 'b': [151.99285888671875, 91.00343322753906], 'frame': 29, 'feature': '15', 'b-a': [-0.007049560546875, 0.00415802001953125], 'time': 0.9860000000000001, 'movement': 0.0037434937107248298}, {'a': [484.0526123046875, 215.99989318847656], 'b': [482.4276428222656, 216.4529266357422], 'frame': 29, 'feature': '16', 'b-a': [-1.624969482421875, 0.453033447265625], 'time': 0.9860000000000001, 'movement': 0.072117179559929107}, {'a': [432.0042724609375, 139.00279235839844], 'b': [431.6622009277344, 138.99868774414062], 'frame': 29, 'feature': '17', 'b-a': [-0.342071533203125, -0.0041046142578125], 'time': 0.9860000000000001, 'movement': 0.014714360114684778}, {'a': [478.06488037109375, 224.00424194335938], 'b': [475.3829650878906, 223.9342803955078], 'frame': 29, 'feature': '18', 'b-a': [-2.681915283203125, -0.0699615478515625], 'time': 0.9860000000000001, 'movement': 0.11665134465899961}, {'a': [220.99217224121094, 148.00503540039062], 'b': [221.0191192626953, 147.93807983398438], 'frame': 29, 'feature': '19', 'b-a': [0.026947021484375, -0.06695556640625], 'time': 0.9860000000000001, 'movement': 0.0054644423452655556}]
Minutes to process: 7.90118351777
Processing: videos/gittes-2-small.mp4
Saving to analysis/optical-flow/gittes-2-small.json
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Minutes to process: 5.45013913314
Processing: videos/gittes-reg-2-small.mp4
Saving to analysis/optical-flow/gittes-reg-2-small.json
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Minutes to process: 4.75200681686
Processing: videos/gittes-reg-small.mp4
Saving to analysis/optical-flow/gittes-reg-small.json
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Minutes to process: 22.1082677007
Processing: videos/nocuts-small.MP4
Saving to analysis/optical-flow/nocuts-small.json
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Minutes to process: 3.26997063557
Processing: videos/nocuts-smaller.mp4
Saving to analysis/optical-flow/nocuts-smaller.json
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Minutes to process: 1.99079976479
Processing: videos/probation-reg-small.mp4
Saving to analysis/optical-flow/probation-reg-small.json
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Minutes to process: 9.59442098538
Processing: videos/probation-shoulder-small.mp4
Saving to analysis/optical-flow/probation-shoulder-small.json
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Minutes to process: 6.21931256851
Processing: videos/steve-walking-small.mp4
Saving to analysis/optical-flow/steve-walking-small.json
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Minutes to process: 1.47125024796
Processing: videos/toy-story-shoulder-2-small.mp4
Saving to analysis/optical-flow/toy-story-shoulder-2-small.json
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Minutes to process: 4.20352231661
Processing: videos/toy-story-shoulder-small.mp4
Saving to analysis/optical-flow/toy-story-shoulder-small.json
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Minutes to process: 4.75023621718
Processing: videos/toy-story-small.mp4
Saving to analysis/optical-flow/toy-story-small.json
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Minutes to process: 4.48213553429






    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

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Processing: videos/equation-2-small.mp4
Saving to analysis/optical-flow/equation-2-small.json
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Minutes to process: 6.212628901
Processing: videos/equation-small.mp4
Saving to analysis/optical-flow/equation-small.json
[{'a': [462.002197265625, 221.00393676757812], 'b': [458.6006164550781, 220.28172302246094], 'frame': 29, 'feature': '0', 'b-a': [-3.401580810546875, -0.7222137451171875], 'time': 0.9860000000000001, 'movement': 0.13158754337627185}, {'a': [487.1059875488281, 185.0153045654297], 'b': [475.3122863769531, 186.43858337402344], 'frame': 29, 'feature': '8', 'b-a': [-11.793701171875, 1.42327880859375], 'time': 0.9860000000000001, 'movement': 0.59260717249507533}, {'a': [484.0526123046875, 215.99989318847656], 'b': [482.4276428222656, 216.4529266357422], 'frame': 29, 'feature': '16', 'b-a': [-1.624969482421875, 0.453033447265625], 'time': 0.9860000000000001, 'movement': 0.072117179559929107}, {'a': [478.06488037109375, 224.00424194335938], 'b': [475.3829650878906, 223.9342803955078], 'frame': 29, 'feature': '18', 'b-a': [-2.681915283203125, -0.0699615478515625], 'time': 0.9860000000000001, 'movement': 0.11665134465899961}, {'a': [389.0019836425781, 143.99427795410156], 'b': [387.4354553222656, 149.49386596679688], 'frame': 29, 'feature': '33', 'b-a': [-1.5665283203125, 5.4995880126953125], 'time': 0.9860000000000001, 'movement': 0.20047155132814279}, {'a': [491.0989074707031, 167.72335815429688], 'b': [431.28192138671875, 167.6227264404297], 'frame': 29, 'feature': '34', 'b-a': [-59.816986083984375, -0.1006317138671875], 'time': 0.9860000000000001, 'movement': 2.5359870470289527}, {'a': [497.02166748046875, 221.98597717285156], 'b': [495.3966369628906, 222.3737335205078], 'frame': 29, 'feature': '38', 'b-a': [-1.625030517578125, 0.38775634765625], 'time': 0.9860000000000001, 'movement': 0.065165932097568588}, {'a': [458.6006164550781, 220.28172302246094], 'b': [322.9026184082031, 128.69493103027344], 'frame': 58, 'feature': '0', 'b-a': [-135.697998046875, -91.5867919921875], 'time': 1.9720000000000002, 'movement': 1.1506730132949026}, {'a': [431.82830810546875, 130.93592834472656], 'b': [148.99652099609375, 132.98233032226562], 'frame': 58, 'feature': '1', 'b-a': [-282.831787109375, 2.0464019775390625], 'time': 1.9720000000000002, 'movement': 1.1041371595909306}, {'a': [149.00003051757812, 133.00100708007812], 'b': [150.0451202392578, 160.00572204589844], 'frame': 58, 'feature': '2', 'b-a': [1.0450897216796875, 27.004714965820312], 'time': 1.9720000000000002, 'movement': 0.0031678111958297387}, {'a': [150.0072021484375, 159.99351501464844], 'b': [216.9941864013672, 100.91950988769531], 'frame': 58, 'feature': '3', 'b-a': [66.98698425292969, -59.074005126953125], 'time': 1.9720000000000002, 'movement': 0.003460600470385418}, {'a': [216.99671936035156, 100.9459457397461], 'b': [144.00294494628906, 147.9891357421875], 'frame': 58, 'feature': '4', 'b-a': [-72.9937744140625, 47.043190002441406], 'time': 1.9720000000000002, 'movement': 0.004621406600625929}, {'a': [144.0144805908203, 148.002685546875], 'b': [16.992782592773438, 221.1549530029297], 'frame': 58, 'feature': '5', 'b-a': [-127.02169799804688, 73.15226745605469], 'time': 1.9720000000000002, 'movement': 0.0030607318486576772}, {'a': [16.98591423034668, 221.1026153564453], 'b': [143.95838928222656, 91.01112365722656], 'frame': 58, 'feature': '6', 'b-a': [126.97247314453125, -130.09149169921875], 'time': 1.9720000000000002, 'movement': 0.0059107534009320986}, {'a': [143.99253845214844, 91.00690460205078], 'b': [473.5601501464844, 185.89671325683594], 'frame': 58, 'feature': '7', 'b-a': [329.567626953125, 94.88980865478516], 'time': 1.9720000000000002, 'movement': 0.0050524741290528564}, {'a': [475.3122863769531, 186.43858337402344], 'b': [279.9999694824219, 181.9967498779297], 'frame': 58, 'feature': '8', 'b-a': [-195.31231689453125, -4.44183349609375], 'time': 1.9720000000000002, 'movement': 0.33615025994367898}, {'a': [280.0120849609375, 181.98980712890625], 'b': [21.009571075439453, 207.0428466796875], 'frame': 58, 'feature': '9', 'b-a': [-259.00250244140625, 25.05303955078125], 'time': 1.9720000000000002, 'movement': 0.0051829451441680529}, {'a': [21.014408111572266, 207.0485382080078], 'b': [231.96180725097656, 133.90133666992188], 'frame': 58, 'feature': '10', 'b-a': [210.94740295410156, -73.14720153808594], 'time': 1.9720000000000002, 'movement': 0.0056551021171516967}, {'a': [231.97787475585938, 133.9452667236328], 'b': [175.03794860839844, 146.96031188964844], 'frame': 58, 'feature': '11', 'b-a': [-56.93992614746094, 13.015045166015625], 'time': 1.9720000000000002, 'movement': 0.00558200651320131}, {'a': [174.9959716796875, 146.96923828125], 'b': [220.97967529296875, 134.86595153808594], 'frame': 58, 'feature': '12', 'b-a': [45.98370361328125, -12.103286743164062], 'time': 1.9720000000000002, 'movement': 0.0048482112299474281}]
Minutes to process: 6.92907663584
Processing: videos/gittes-2-small.mp4
Saving to analysis/optical-flow/gittes-2-small.json
[{'a': [495.6469421386719, 102.82315826416016], 'b': [466.98236083984375, 117.00455474853516], 'frame': 29, 'feature': '1', 'b-a': [-28.664581298828125, 14.181396484375], 'time': 0.9860000000000001, 'movement': 1.6709280034591412}, {'a': [23.544391632080078, 190.168212890625], 'b': [422.0282287597656, 209.98008728027344], 'frame': 29, 'feature': '2', 'b-a': [398.48382568359375, 19.811874389648438], 'time': 0.9860000000000001, 'movement': 1.7020365254259027}, {'a': [422.0003967285156, 209.99949645996094], 'b': [309.97662353515625, 109.01148223876953], 'frame': 29, 'feature': '3', 'b-a': [-112.02377319335938, -100.9880142211914], 'time': 0.9860000000000001, 'movement': 0.0034141076030209661}, {'a': [309.9993896484375, 108.9990234375], 'b': [312.513671875, 140.00946044921875], 'frame': 29, 'feature': '4', 'b-a': [2.5142822265625, 31.01043701171875], 'time': 0.9860000000000001, 'movement': 0.15379373751408099}, {'a': [317.0028381347656, 140.00357055664062], 'b': [215.95245361328125, 222.9853515625], 'frame': 29, 'feature': '5', 'b-a': [-101.05038452148438, 82.98178100585938], 'time': 0.9860000000000001, 'movement': 0.012301913924226216}, {'a': [215.99862670898438, 223.00048828125], 'b': [469.0154724121094, 153.50283813476562], 'frame': 29, 'feature': '6', 'b-a': [253.016845703125, -69.49765014648438], 'time': 0.9860000000000001, 'movement': 1.5506208427491244}, {'a': [494.53729248046875, 177.280029296875], 'b': [315.9908142089844, 114.0401840209961], 'frame': 29, 'feature': '7', 'b-a': [-178.54647827148438, -63.239845275878906], 'time': 0.9860000000000001, 'movement': 0.22767837430703744}, {'a': [316.0011901855469, 113.99822235107422], 'b': [259.96124267578125, 221.01087951660156], 'frame': 29, 'feature': '8', 'b-a': [-56.039947509765625, 107.01265716552734], 'time': 0.9860000000000001, 'movement': 0.0037970082485117018}, {'a': [259.9996337890625, 220.99691772460938], 'b': [269.99664306640625, 213.024658203125], 'frame': 29, 'feature': '9', 'b-a': [9.99700927734375, -7.972259521484375], 'time': 0.9860000000000001, 'movement': 0.0032928199999050461}, {'a': [269.9993896484375, 213.00039672851562], 'b': [391.01080322265625, 95.03973388671875], 'frame': 29, 'feature': '10', 'b-a': [121.01141357421875, -117.96066284179688], 'time': 0.9860000000000001, 'movement': 0.0045565086896611571}, {'a': [390.999755859375, 94.99905395507812], 'b': [21.293365478515625, 210.51226806640625], 'frame': 29, 'feature': '11', 'b-a': [-369.7063903808594, 115.51321411132812], 'time': 0.9860000000000001, 'movement': 0.03597246240503315}, {'a': [21.00519561767578, 209.9949188232422], 'b': [298.9660949707031, 117.02423095703125], 'frame': 29, 'feature': '12', 'b-a': [277.9609069824219, -92.97068786621094], 'time': 0.9860000000000001, 'movement': 0.0071005664930421991}, {'a': [299.00347900390625, 116.99942016601562], 'b': [242.98818969726562, 205.01797485351562], 'frame': 29, 'feature': '13', 'b-a': [-56.015289306640625, 88.0185546875], 'time': 0.9860000000000001, 'movement': 0.0038629740974384136}, {'a': [243.00094604492188, 204.99920654296875], 'b': [422.0039367675781, 194.001708984375], 'frame': 29, 'feature': '14', 'b-a': [179.00299072265625, -10.99749755859375], 'time': 0.9860000000000001, 'movement': 0.0036508827149097263}, {'a': [422.0011901855469, 193.9978790283203], 'b': [70.98041534423828, 215.99411010742188], 'frame': 29, 'feature': '15', 'b-a': [-351.0207824707031, 21.996231079101562], 'time': 0.9860000000000001, 'movement': 0.0029972950799454904}, {'a': [70.99855041503906, 215.99923706054688], 'b': [321.0059814453125, 206.00369262695312], 'frame': 29, 'feature': '16', 'b-a': [250.00743103027344, -9.99554443359375], 'time': 0.9860000000000001, 'movement': 0.0043054419491391884}, {'a': [320.9989318847656, 206.00308227539062], 'b': [248.98077392578125, 225.9912109375], 'frame': 29, 'feature': '17', 'b-a': [-72.01815795898438, 19.988128662109375], 'time': 0.9860000000000001, 'movement': 0.0031545974217869087}, {'a': [248.99781799316406, 225.9985809326172], 'b': [267.9767761230469, 197.00918579101562], 'frame': 29, 'feature': '18', 'b-a': [18.978958129882812, -28.989395141601562], 'time': 0.9860000000000001, 'movement': 0.0035976976806554816}, {'a': [267.9964904785156, 196.99964904785156], 'b': [482.3688049316406, 205.6590118408203], 'frame': 29, 'feature': '19', 'b-a': [214.372314453125, 8.65936279296875], 'time': 0.9860000000000001, 'movement': 0.11772162487915043}, {'a': [481.0006408691406, 208.0017852783203], 'b': [394.025146484375, 215.982666015625], 'frame': 29, 'feature': '20', 'b-a': [-86.97549438476562, 7.9808807373046875], 'time': 0.9860000000000001, 'movement': 0.0043746668540326685}]
Minutes to process: 5.21359588305
Processing: videos/gittes-reg-2-small.mp4
Saving to analysis/optical-flow/gittes-reg-2-small.json
[{'a': [488.6524658203125, 104.18718719482422], 'b': [476.2348327636719, 104.88154602050781], 'frame': 29, 'feature': '0', 'b-a': [-12.417633056640625, 0.6943588256835938], 'time': 0.9860000000000001, 'movement': 0.47654319289622121}, {'a': [478.6061706542969, 148.50587463378906], 'b': [476.6455993652344, 151.22048950195312], 'frame': 29, 'feature': '2', 'b-a': [-1.9605712890625, 2.7146148681640625], 'time': 0.9860000000000001, 'movement': 0.29431149981723265}, {'a': [8.400105476379395, 155.9936065673828], 'b': [480.60589599609375, 132.28140258789062], 'frame': 29, 'feature': '6', 'b-a': [472.2057800292969, -23.712203979492188], 'time': 0.9860000000000001, 'movement': 0.65460505876248576}, {'a': [1.6982417106628418, 167.85667419433594], 'b': [298.00933837890625, 117.0051498413086], 'frame': 29, 'feature': '7', 'b-a': [296.31109619140625, -50.851524353027344], 'time': 0.9860000000000001, 'movement': 0.29993658023083519}, {'a': [483.6318359375, 133.05264282226562], 'b': [422.011962890625, 196.90069580078125], 'frame': 29, 'feature': '8', 'b-a': [-61.619873046875, 63.848052978515625], 'time': 0.9860000000000001, 'movement': 0.18821927548610959}, {'a': [297.9986267089844, 117.00003814697266], 'b': [216.14730834960938, 223.1031951904297], 'frame': 29, 'feature': '9', 'b-a': [-81.851318359375, 106.10315704345703], 'time': 0.9860000000000001, 'movement': 0.0029370694648040907}, {'a': [421.99981689453125, 197.01016235351562], 'b': [390.9574890136719, 94.9243392944336], 'frame': 29, 'feature': '10', 'b-a': [-31.042327880859375, -102.08582305908203], 'time': 0.9860000000000001, 'movement': 0.0071608245489187539}, {'a': [216.00048828125, 222.99510192871094], 'b': [260.2191162109375, 221.05657958984375], 'frame': 29, 'feature': '11', 'b-a': [44.2186279296875, -1.9385223388671875], 'time': 0.9860000000000001, 'movement': 0.0085281562080189322}, {'a': [391.0025329589844, 95.00179290771484], 'b': [20.87718963623047, 209.9384002685547], 'frame': 29, 'feature': '12', 'b-a': [-370.1253356933594, 114.93660736083984], 'time': 0.9860000000000001, 'movement': 0.0055172404611547442}, {'a': [260.000732421875, 220.9983673095703], 'b': [315.9952087402344, 114.005126953125], 'frame': 29, 'feature': '13', 'b-a': [55.994476318359375, -106.99324035644531], 'time': 0.9860000000000001, 'movement': 0.0092720407912314957}, {'a': [21.00065040588379, 210.004638671875], 'b': [243.1641387939453, 205.0838165283203], 'frame': 29, 'feature': '14', 'b-a': [222.16348266601562, -4.9208221435546875], 'time': 0.9860000000000001, 'movement': 0.0073095719281840938}, {'a': [316.00146484375, 113.99929809570312], 'b': [270.200439453125, 213.03465270996094], 'frame': 29, 'feature': '15', 'b-a': [-45.801025390625, 99.03535461425781], 'time': 0.9860000000000001, 'movement': 0.0038993651785482332}, {'a': [242.9973602294922, 205.0006561279297], 'b': [16.784820556640625, 215.9591827392578], 'frame': 29, 'feature': '16', 'b-a': [-226.21253967285156, 10.958526611328125], 'time': 0.9860000000000001, 'movement': 0.008844599031425756}, {'a': [269.9988708496094, 212.99700927734375], 'b': [71.78205871582031, 216.0328826904297], 'frame': 29, 'feature': '17', 'b-a': [-198.21681213378906, 3.0358734130859375], 'time': 0.9860000000000001, 'movement': 0.0096094216534201513}, {'a': [1.6138792037963867, 132.89422607421875], 'b': [249.2402801513672, 226.07252502441406], 'frame': 29, 'feature': '18', 'b-a': [247.62640380859375, 93.17829895019531], 'time': 0.9860000000000001, 'movement': 0.245393386030377}, {'a': [16.995250701904297, 216.00294494628906], 'b': [497.894775390625, 208.93142700195312], 'frame': 29, 'feature': '19', 'b-a': [480.8995361328125, -7.0715179443359375], 'time': 0.9860000000000001, 'movement': 0.010635259088554323}, {'a': [72.00171661376953, 216.00143432617188], 'b': [223.16404724121094, 220.1123809814453], 'frame': 29, 'feature': '20', 'b-a': [151.16232299804688, 4.1109466552734375], 'time': 0.9860000000000001, 'movement': 0.011227203127606932}, {'a': [248.99757385253906, 225.99546813964844], 'b': [487.8302917480469, 214.90176391601562], 'frame': 29, 'feature': '21', 'b-a': [238.8327178955078, -11.093704223632812], 'time': 0.9860000000000001, 'movement': 0.012624345727842944}, {'a': [497.99847412109375, 209.00270080566406], 'b': [427.00323486328125, 105.91051483154297], 'frame': 29, 'feature': '22', 'b-a': [-70.9952392578125, -103.0921859741211], 'time': 0.9860000000000001, 'movement': 0.010349861426471636}, {'a': [222.99913024902344, 219.99667358398438], 'b': [483.32232666015625, 165.25558471679688], 'frame': 29, 'feature': '23', 'b-a': [260.32318115234375, -54.7410888671875], 'time': 0.9860000000000001, 'movement': 0.11405931143009843}]
Minutes to process: 4.46830123266
Processing: videos/gittes-reg-small.mp4






    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-20-d066033e21c5> in <module>()
     11 
     12     start = time.time()
---> 13     recorded_important_points = find_important_points_for_fn(fn)
     14     save_important_points_fn(fn, recorded_important_points)
     15     print "Minutes to process:", (time.time() - start)/60.0

<ipython-input-15-d6995a4e218d> in find_important_points_for_fn(fn)
    108             cv2.imshow('frame',img)
    109 
--> 110             k = cv2.waitKey(30) & 0xff
    111             if k == 27:
    112                 break

KeyboardInterrupt: 

In [ ]:

    

# hog pedestrian detection from - (http://arijitgeek.me/index.php/201
# 6/06/26/opencv-python-people-detect-in-video-feed/)
# lots of false positives!!

import numpy as np
import cv2

fn = "videos/equation-2.mp4"

def draw_detections(img, rects, thickness = 1):
    for x, y, w, h in rects:
        # the HOG detector returns slightly larger rectangles than the real objects.
        # so we slightly shrink the rectangles to get a nicer output.
        pad_w, pad_h = int(0.15*w), int(0.05*h)
        cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
    
hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
cap=cv2.VideoCapture(fn)
while True:
    _,frame=cap.read()
    found,w=hog.detectMultiScale(frame, winStride=(8,8), padding=(32,32), scale=1.05)
    draw_detections(frame,found)
    cv2.imshow('feed',frame)
    ch = 0xFF & cv2.waitKey(30)
    if ch == 27:
      break
cv2.destroyAllWindows()

# dense features
# from - http://docs.opencv.org/3.2.0/d7/d8b/tutorial_py_lucas_kanade.html

fn = "videos/equation-2-small.mp4"

import cv2
import numpy as np
cap = cv2.VideoCapture(fn)
ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
while(1):
    ret, frame2 = cap.read()
    next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
    flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
    hsv[...,0] = ang*180/np.pi/2
    hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
    bgr = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
    cv2.imshow('frame2',bgr)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break
    elif k == ord('s'):
        cv2.imwrite('opticalfb.png',frame2)
        cv2.imwrite('opticalhsv.png',bgr)
    prvs = next
cap.release()
cv2.destroyAllWindows()


    

In [23]:

    

cv2.waitKey(1)
        cv2.destroyAllWindows()
        cv2.waitKey(1)


    

    
Out[23]:





-1

In [ ]: