import Image import os os.system( "wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz" ) os.system("tar -xzvf BSDS300-images.tgz") imgs = glob.glob("BSDS300/images/test/*.jpg") for n, i in enumerate(imgs): f = i.split('/')[-1].split('.')[0] print n, f k = mmreadgray(i) size = k.shape scale = 4000.0 / min(size) newsize = (int(size[0] * scale), int(size[1] * scale)) img = (mmhmin(k, 50) > 128).astype(numpy.uint8) * 255 img = mmareaclose(img, 20) img = mmareaopen(img, 20) pil_img = Image.fromarray(img, "L") pil_img = pil_img.resize(newsize) pil_img.save("output/" + f + ".png") os.system("convert -compress none %s %s" % ("output/" + f + ".png", "output/" + f + ".pgm")) import sys sys.exit(0)
from morph import mmlabel, mmreadgray, mmhmin, mmthreshad, mmlabelflat, mmareaclose, mmareaopen import glob import Image import os os.system("wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz") os.system("tar -xzvf BSDS300-images.tgz") imgs = glob.glob("BSDS300/images/test/*.jpg") for n,i in enumerate(imgs): f = i.split('/')[-1].split('.')[0] print n, f k = mmreadgray(i) size = k.shape scale = 4000.0/min(size) newsize = (int(size[0]*scale), int(size[1]*scale)) img = (mmhmin(k, 50) > 128).astype(numpy.uint8)*255 img = mmareaclose(img, 20) img = mmareaopen(img, 20) pil_img = Image.fromarray(img, "L") pil_img = pil_img.resize(newsize) pil_img.save( "output/"+f+".png" ) os.system("convert -compress none %s %s" % ("output/"+f+".png", "output/"+f+".pgm")) import sys sys.exit(0)
return [numbercc, gold_serial, uf_gpu, uf_hybrid, lequiv_gpu] l_uf = numpy.zeros(len(imgs)) l_uf_hybrid = numpy.zeros(len(imgs)) l_lequiv = numpy.zeros(len(imgs)) l_stephano = numpy.zeros(len(imgs)) for n, i in enumerate(imgs): RESULTS = [] f = os.path.split(OUTPUT_SIMPLE + i)[1].split(".")[0] res = run(OUTPUT_SIMPLE + f) k = (mmreadgray(OUTPUT_SIMPLE + f + ".png") > 0) v1 = [] for j in range(10): t0 = time.time() lbl = mmlabel(k) t1 = time.time() v1.append(1000 * (t1 - t0)) RESULTS = res + [min(v1)] l_uf[n] = RESULTS[2] l_uf_hybrid[n] = RESULTS[3] l_lequiv[n] = RESULTS[4] l_stephano[n] = RESULTS[5]
return [numbercc, gold_serial, uf_gpu, uf_hybrid, lequiv_gpu] l_uf = numpy.zeros(len(imgs)) l_uf_hybrid = numpy.zeros(len(imgs)) l_lequiv = numpy.zeros(len(imgs)) l_stephano = numpy.zeros(len(imgs)) for n,i in enumerate(imgs): RESULTS = [] f = os.path.split(OUTPUT_SIMPLE+i)[1].split(".")[0] res = run(OUTPUT_SIMPLE+f) k = (mmreadgray(OUTPUT_SIMPLE+f+".png") > 0) v1 = [] for j in range(10): t0 = time.time() lbl = mmlabel(k) t1 = time.time() v1.append(1000*(t1-t0)) RESULTS = res+[min(v1)] l_uf[n] = RESULTS[2] l_uf_hybrid[n] = RESULTS[3] l_lequiv[n] = RESULTS[4] l_stephano[n] = RESULTS[5]