def do_test_detect_motion(self, W, H):
BPP = 4
#W, H, BPP = 2, 4, 4
LEN = W * H * BPP
import numpy as np
try:
na1 = np.random.randint(255, size=LEN, dtype="uint8")
except TypeError as e:
#older numpy version may not have dtype argument..
#and may not accept 64-bit values
print("skipping motion detection test")
print(" because of incompatible numpy version: %s" % e)
try:
print(" numpy %s" % np.version.version)
except:
pass
return
def tobytes(a):
try:
return a.tobytes()
except:
#older versions of numpy (ie: centos7)
return a.tostring()
buf1 = tobytes(na1)
#push first image:
sd = motion.ScrollData(0, 0, W, H)
#make a new "image" shifted N lines:
for N in (1, 2, 20, 100):
if N>H//2:
break
sd.update(buf1, 0, 0, W, H, W*BPP, BPP)
log("picture of height %i scrolled by %i", H, N)
na2 = np.roll(na1, -N*W*BPP)
buf2 = tobytes(na2)
start = monotonic_time()
sd.update(buf2, 0, 0, W, H, W*BPP, BPP)
end = monotonic_time()
if SHOW_PERF:
log.info("hashed image %ix%i (%.1fMB) in %4.2f ms" % (W, H, len(buf2)//1024//1024, 1000.0*(end-start)))
start = monotonic_time()
sd.calculate()
sd_data = sd.get_scroll_values(1)
scrolls, non_scrolls = sd_data
log("scroll values=%s", dict(scrolls))
log("non scroll values=%s", dict(non_scrolls))
end = monotonic_time()
if SHOW_PERF:
log.info("calculated distances %4i^2 in %5.2f ms" % (H, 1000.0*(end-start)))
line_defs = scrolls.get(-N, {})
linecount = sum(line_defs.values())
assert linecount>0, "could not find distance %i in %s" % (N, line_defs)
assert linecount == (H-N), "expected to match %i lines but got %i" % (H-N, linecount)
if False:
import binascii
log("na1:\n%s" % binascii.hexlify(tobytes(na1)))
log("na2:\n%s" % binascii.hexlify(tobytes(na2)))
np.set_printoptions(threshold=np.inf)
log("na1:\n%s" % (na1, ))
log("na2:\n%s" % (na2, ))
def do_calculate_distances(self,
rect,
array1,
array2,
min_hits=2,
max_distance=1000):
sd = motion.ScrollData(*rect)
sd._test_update(array1)
sd._test_update(array2)
sd.calculate(max_distance)
return sd.get_scroll_values(min_hits)
def do_test_detect_motion(self, W, H):
BPP = 4
#W, H, BPP = 2, 4, 4
LEN = W * H * BPP
import numpy as np
na1 = np.random.randint(255, size=LEN, dtype="uint8")
def tobytes(a):
return a.tobytes()
buf1 = tobytes(na1)
#push first image:
sd = motion.ScrollData(0, 0, W, H)
#make a new "image" shifted N lines:
for N in (1, 2, 20, 100):
if N>H//2:
break
sd.update(buf1, 0, 0, W, H, W*BPP, BPP)
log("picture of height %i scrolled by %i", H, N)
na2 = np.roll(na1, -N*W*BPP)
buf2 = tobytes(na2)
start = monotonic_time()
sd.update(buf2, 0, 0, W, H, W*BPP, BPP)
end = monotonic_time()
if SHOW_PERF:
log.info("hashed image %ix%i (%.1fMB) in %4.2f ms" % (W, H, len(buf2)//1024//1024, 1000.0*(end-start)))
start = monotonic_time()
sd.calculate()
sd_data = sd.get_scroll_values(1)
scrolls, non_scrolls = sd_data
log("scroll values=%s", dict(scrolls))
log("non scroll values=%s", dict(non_scrolls))
end = monotonic_time()
if SHOW_PERF:
log.info("calculated distances %4i^2 in %5.2f ms" % (H, 1000.0*(end-start)))
line_defs = scrolls.get(-N, {})
linecount = sum(line_defs.values())
assert linecount>0, "could not find distance %i in %s" % (N, line_defs)
assert linecount == (H-N), "expected to match %i lines but got %i" % (H-N, linecount)
def test_csum_data(self):
a1 = [
5992220345606009987,
15040563112965825180,
420530012284267555,
3380071419019115782,
14243596304267993264,
834861281570233459,
10803583843784306120,
1379296002677236226,
11874402007024898787,
18061820378193118025,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
6048597477520792617,
2736806572525204051,
16630099595908746458,
10194355114249600963,
16726784880639428445,
10866892264854763364,
6367321356510949102,
16626509354687956371,
6309605599425761357,
6893409879058778343,
5414245501850544038,
10339135854757169820,
8701041795744152980,
3604633436491088815,
9865399393235410477,
10031306284568036792,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
11266963446837574547,
17157005122993541799,
5218869126146608853,
13274228147453099388,
16342723934713827717,
2435034235422505275,
3689766606612767057,
13721141386368216492,
14859793948180065358,
6883776362280179367,
14582348771255332968,
15418692344756373599,
10241123668249748621,
197976484773286461,
14610077842739908751,
9629342716869811747,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
6301677547777858738,
13481745547040629090,
11082728931134194933,
3515047519092751608,
17530992646520472518,
11525573497958613731,
6186650688264051723,
10053681394182111520,
7507461626261938488,
3136410141592758381,
18320341500820189028,
7224279069641644876,
76220613438872403,
12174575413544881100,
7769327179604108765,
4993163530803732307,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
1011212212406598056,
12369511552952147752,
17332471340354818353,
5562967289984763417,
7276816103432910616,
9095502394548196500,
3966866363266810705,
15115893782344445994,
2470115778756702218,
11300572931034497831,
13356453083734411092,
12682463388000998283,
12461900100761490812,
16565659067973398797,
16700371844333341655,
13475749720883007409,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
15095743182479501355,
16652551598896547263,
18117428461752083731,
16517651160080181273,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
2620400469557574299,
7552116755125697612,
3191732720857892986,
15697817096682717297,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
]
a2 = [
16517651160080181273,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
16482769665263024512,
2620400469557574299,
7552116755125697612,
3191732720857892986,
15697817096682717297,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
7425237873317005741,
15881577514219781533,
5244943483479698162,
1645884179624549962,
6833306483329956671,
3142507118889544939,
14496593126061659900,
4782446320116037220,
11121580325383588737,
5128902123802403342,
14539804846999948736,
3999126996485638007,
6071163207581089360,
275311871111368509,
1419512211527079444,
16496147506624837932,
9366935943282992292,
16641602392096942222,
5312414525355881355,
6512670471206739810,
14669207905734636057,
14669207905734636057,
9515221130600033946,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
16962147477217322879,
17777684172941730501,
5134598006302276024,
4495650412094508491,
14496320858648784912,
5882882193233282408,
13142401013874562815,
17213868142308207279,
5589927236057965940,
4529401611344340209,
3205874171513572790,
9555164747562437240,
14669207905734636057,
14669207905734636057,
14669207905734636057,
9080427549593249618,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
8165205005918492527,
13352578771313229684,
11590125678725701957,
2006171165294962460,
5731472049560910928,
7815231195191982982,
5992220345606009987,
15040563112965825180,
420530012284267555,
3380071419019115782,
14243596304267993264,
834861281570233459,
10803583843784306120,
1379296002677236226,
11874402007024898787,
18061820378193118025,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
6048597477520792617,
2736806572525204051,
16630099595908746458,
10194355114249600963,
16726784880639428445,
10866892264854763364,
6367321356510949102,
16626509354687956371,
6309605599425761357,
6893409879058778343,
5414245501850544038,
10339135854757169820,
8701041795744152980,
3604633436491088815,
9865399393235410477,
10031306284568036792,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
14669207905734636057,
11266963446837574547,
17157005122993541799,
5218869126146608853,
13274228147453099388,
16342723934713827717,
2435034235422505275,
3689766606612767057,
13721141386368216492,
14859793948180065358,
]
#distances = motion.scroll_distances(a1[100:400], a2[100:400], 2, 1000)
sd = motion.ScrollData(0, 0, 1050, len(a1))
sd._test_update(a1)
sd._test_update(a2)
sd.calculate(1000)
scroll, count = sd.get_best_match()
wh = len(a1)
log("best match: %s" % ((scroll, count), ))
x, y, w, h = 0, 0, 1050, 1151
raw_scroll, non_scroll = sd.get_scroll_values()
assert len(non_scroll) > 0
scrolls = []
def hexstr(v):
return hex(v).lstrip("0x").rstrip("L")
for i in range(wh):
log("%2i: %16s %16s" % (i, hexstr(a1[i]), hexstr(a2[i])))
for scroll, line_defs in raw_scroll.items():
if scroll == 0:
continue
for line, count in line_defs.items():
assert y + line + scroll >= 0, "cannot scroll rectangle by %i lines from %i+%i" % (
scroll, y, line)
assert y + line + scroll <= wh, "cannot scroll rectangle %i high by %i lines from %i+%i (window height is %i)" % (
count, scroll, y, line, wh)
scrolls.append((x, y + line, w, count, 0, scroll))
