def _get_ecp_day_night_9_priors(): priors = [[0.5728529907421875, 0.13943622409895834], [0.41761617583007815, 0.09156660707291667], [0.3015263176855469, 0.06248444700520834], [0.22101856140625, 0.042888710765625], [0.1533158565527344, 0.031196821406250002], [0.11255495265625, 0.021566710822916668], [0.07823327209960937, 0.015212825187500001], [0.0533416983203125, 0.010216603067708333], [0.0332035418359375, 0.006413999807291667]] # # in pixels: # priors = [[586.60146252, 267.71755027], # [427.63896405, 175.80788558], # [308.76294931, 119.97013825], # [226.32300688, 82.34632467], # [156.99543711, 59.8978971], # [115.25627152, 41.40808478], # [80.11087063, 29.20862436], # [54.62189908, 19.61587789], # [34.00042684, 12.31487963]] priors_32 = [data.Prior(h=p[0], w=p[1]) for p in priors[:3]] priors_16 = [data.Prior(h=p[0], w=p[1]) for p in priors[3:6]] priors_8 = [data.Prior(h=p[0], w=p[1]) for p in priors[6:]] return { 32: priors_32, 16: priors_16, 8: priors_8, }
def _get_ecp_night_9_priors(): priors = [ [0.6197282176953125, 0.14694562146874998], [0.4243941425683594, 0.09687759120833334], [0.3103862368359375, 0.06362734035416667], [0.23494613041992188, 0.043568554453125], [0.1634832566796875, 0.03293052755208333], [0.12444031231445313, 0.023274527578125], [0.08800429220703125, 0.016930080526041665], [0.06101826478515625, 0.011638404229166668], [0.03925641140625, 0.007475639645833334], ] # # in pixels: # priors = [[634.60169492, 282.13559322], # [434.57960199, 186.00497512], # [317.83550652, 122.16449348], # [240.58483755, 83.65162455], # [167.40685484, 63.2266129], # [127.42687981, 44.68709295], # [90.11639522, 32.50575461], # [62.48270314, 22.34573612], # [40.19856528, 14.35322812]] priors_32 = [data.Prior(h=p[0], w=p[1]) for p in priors[:3]] priors_16 = [data.Prior(h=p[0], w=p[1]) for p in priors[3:6]] priors_8 = [data.Prior(h=p[0], w=p[1]) for p in priors[6:]] return { 32: priors_32, 16: priors_16, 8: priors_8, }
def _get_ecp_9_priors(): priors = [ [0.56643243, 0.13731691], [0.41022839, 0.09028599], [0.30508716, 0.06047965], [0.20774711, 0.04376083], [0.15475611, 0.02996197], [0.10878717, 0.02149197], [0.07694039, 0.01488527], [0.05248527, 0.01007212], [0.03272104, 0.00631827], ] # # in pixels: # priors = [[580.02680832, 263.64846719999997], # [420.07387136, 173.3491008], # [312.40925184, 116.120928], # [212.73304064, 84.0207936], # [158.47025664, 57.5269824], # [111.39806208, 41.264582399999995], # [78.78695936, 28.5797184], # [53.74491648, 19.338470400000002], # [33.50634496, 12.1310784]] priors_32 = [data.Prior(h=p[0], w=p[1]) for p in priors[:3]] priors_16 = [data.Prior(h=p[0], w=p[1]) for p in priors[3:6]] priors_8 = [data.Prior(h=p[0], w=p[1]) for p in priors[6:]] return { 32: priors_32, 16: priors_16, 8: priors_8, }
def _get_ecp_with_bic_9_priors(): priors = [ [0.5541169062011718, 0.15767184942708334], [0.3872792363671875, 0.08849276056770834], [0.27297898112304686, 0.05552458755208333], [0.18570756796875, 0.034849724458333335], [0.13080457012695312, 0.052510955223958336], [0.12203939466796875, 0.02422101765625], [0.083340965234375, 0.01635016602083333], [0.055563667021484374, 0.010672233619791667], [0.03409191838867188, 0.006481136984375], ] # # in pixels: # priors = [[567.41571195, 302.7299509], # [396.57393804, 169.90610029], # [279.53047667, 106.6072081], # [190.1645496, 66.91147096], # [133.94387981, 100.82103403], # [124.96834014, 46.5043539], # [85.3411484, 31.39231876], # [56.89719503, 20.49068855], # [34.91012443, 12.44378301]] priors_32 = [data.Prior(h=p[0], w=p[1]) for p in priors[:3]] priors_16 = [data.Prior(h=p[0], w=p[1]) for p in priors[3:6]] priors_8 = [data.Prior(h=p[0], w=p[1]) for p in priors[6:]] return { 32: priors_32, 16: priors_16, 8: priors_8, }
def _get_city_persons_9_priors(): priors = [[495.27, 203.83], [297.84, 122.19], [197.44, 81.48], [141.07, 58.5], [102.72, 43.1], [75.78, 31.66], [54.24, 23.19], [37.55, 16.15], [22.55, 10.09]] priors = [[p[0] / 1024., p[1] / 2048.] for p in priors ] # priors are calculated for original citypersons img size priors_32 = [data.Prior(h=p[0], w=p[1]) for p in priors[:3]] priors_16 = [data.Prior(h=p[0], w=p[1]) for p in priors[3:6]] priors_8 = [data.Prior(h=p[0], w=p[1]) for p in priors[6:]] return { 32: priors_32, 16: priors_16, 8: priors_8, }
def img_size_and_priors_if_crop( config): # TODO fn name is not very descriptive img_size = config['crop_img_size'] if config['crop'] else config[ 'full_img_size'] priors = config['priors'] if config['crop']: # priors are always defined for the full img => need to rescale if we crop the image scale_h = config['full_img_size'][0] / float( config['crop_img_size'][0]) scale_w = config['full_img_size'][1] / float( config['crop_img_size'][1]) for stride, prs in priors.items(): priors[stride] = [ data.Prior(h=p.h * scale_h, w=p.w * scale_w) for p in prs ] return img_size, priors