#!/usr/bin/env python # This file is part of Xpra. # Copyright (C) 2016-2019 Antoine Martin # Xpra is released under the terms of the GNU GPL v2, or, at your option, any # later version. See the file COPYING for details. import unittest from zlib import crc32 from xpra.util import envbool from xpra.os_util import monotonic_time, strtobytes try: from xpra.server.window import motion log = motion.log #@UndefinedVariable except ImportError: motion = None SHOW_PERF = envbool("XPRA_SHOW_PERF") class TestMotion(unittest.TestCase): def calculate_distances(self, array1, array2, min_hits=2, max_distance=1000): assert len(array1)==len(array2) rect = (0, 0, 1, len(array1)) return self.do_calculate_distances(rect, array1, array2, min_hits, max_distance) 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 test_simple(self): self.calculate_distances([0], [1], 0, 100) def test_match_distance(self): def t(a1, a2, distance, matches): scrolls = self.calculate_distances(a1, a2, 0)[0] line_defs = scrolls.get(distance) assert line_defs, "distance %i not found in scroll data: %s for a1=%s, a2=%s" % (distance, scrolls, a1, a2) linecount = sum(line_defs.values()) assert linecount==matches, "expected %i matches for distance=%i but got %i for a1=%s, a2=%s, result=%s" % ( matches, distance, linecount, a1, a2, line_defs) for N in (motion.MIN_LINE_COUNT+1, 10, 100): a = range(1, N+1) t(a, a, 0, N) #identity: all match a = [1]*N t(a, a, 0, N) #from a1 to a2: shift by 2, get 6 hits t([3, 4, 5, 6, 7, 8, 9, 10], [1, 2, 3, 4, 5, 6, 7, 8], 2, 6) #from a2 to a1: shift by -2, get 6 hits t([1, 2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9, 10], -2, 6) N = 100 S = 1 a1 = range(S, S+N) for M in (motion.MIN_LINE_COUNT, motion.MIN_LINE_COUNT+1, motion.MIN_LINE_COUNT+10, 90): a2 = range(M, M+N) t(a1, a2, S-M, S+N-M) t(a2, a1, M-S, S+N-M) def test_calculate_distances(self): array1 = [crc32(strtobytes(x)) for x in (1234, b"abc", 99999)] array2 = array1[:] d = self.calculate_distances(array1, array2, 1)[0] assert len(d)==1 and sum(d[0].values())==len(array1), "expected %i matches but got %s" % (len(array1), d[0]) array1 = range(1, 5) array2 = range(2, 6) d = self.calculate_distances(array1, array2)[0] assert len(d)==1, "expected 1 match but got: %s" % len(d) common = set(array1).intersection(set(array2)) assert -1 in d, "expected distance of -1 but got: %s" % d.keys() linecount = sum(d.get(-1, {}).values()) assert linecount==len(common), "expected %i hits but got: %s" % (len(common), linecount) def cdf(v1, v2): try: self.calculate_distances(v1, v2, 1) except Exception: return else: raise Exception("calculate_distances should have failed for values: %s, %s" % (v1, v2)) cdf(None, None) cdf([], None) cdf(None, []) cdf([1, 2], [1]) #performance: N = 4096 start = monotonic_time() array1 = range(N) array2 = [N*2-x*2 for x in range(N)] d = self.calculate_distances(array1, array2, 1)[0] end = monotonic_time() if SHOW_PERF: log.info("calculate_distances %4i^2 in %5.1f ms" % (N, (end-start)*1000)) def test_detect_motion(self): self.do_test_detect_motion(5, 5) self.do_test_detect_motion(1920, 1080) 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) #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 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) x, y, w = 0, 0, 1050 h = len(a1) sd = motion.ScrollData(x, y, w, h) sd.test_update(a1) sd.test_update(a2) sd.calculate(1000) scroll, count = sd.get_best_match() log("best match: %s" % ((scroll, count),)) 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(h): 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<=h, "cannot scroll rectangle %i high by %i lines from %i+%i (window height is %i)" % ( count, scroll, y, line, h) scrolls.append((x, y+line, w, count, 0, scroll)) def main(): if motion: unittest.main() if __name__ == '__main__': main()