def test_cuda_array(pic): pic_h = cp.push_host(pic) # downsample pic_h tmp = (np.array([pic_h.h,pic_h.w])/2).astype('uint32') down = cp.dev_matrix_rmf(tmp[0],tmp[1]) ca = cp.dev_cuda_array_f(pic_h.h,pic_h.w) ca.assign(pic_h) cp.gaussian_pyramid_downsample(down,ca) # upsample downsampled ca = cp.dev_cuda_array_f(down.h,down.w) ca.assign(down) up = cp.dev_matrix_rmf(pic_h.h, pic_h.w) cp.gaussian_pyramid_upsample(up,ca) print cp.pull(up) plt.matshow(cp.pull(down)) plt.title("Downsampled") plt.matshow(pic) plt.title("Original") plt.matshow(cp.pull(up)) plt.title("Upsampled") plt.show() ca.dealloc()
def test_cuda_array(pic): pic_h = cp.push_host(pic) # downsample pic_h tmp = (np.array([pic_h.h, pic_h.w]) / 2).astype('uint32') down = cp.dev_matrix_rmf(tmp[0], tmp[1]) ca = cp.dev_cuda_array_f(pic_h.h, pic_h.w) ca.assign(pic_h) cp.gaussian_pyramid_downsample(down, ca) # upsample downsampled ca = cp.dev_cuda_array_f(down.h, down.w) ca.assign(down) up = cp.dev_matrix_rmf(pic_h.h, pic_h.w) cp.gaussian_pyramid_upsample(up, ca) print cp.pull(up) plt.matshow(cp.pull(down)) plt.title("Downsampled") plt.matshow(pic) plt.title("Original") plt.matshow(cp.pull(up)) plt.title("Upsampled") plt.show() ca.dealloc()
def gray_test(ni): src = cp.push(to_cmuc(np.tile(ni,(1,4)))) dst = cp.dev_matrix_cmf(src.h,src.w) cp.fill(dst,0) cp.image_move(dst,src,128,128,1,-10,-4) res = cp.pull(dst) #set_trace() plt.matshow(res[0:128**2,0].reshape(128,128)) plt.colorbar() plt.show()
def gray_test(ni): src = cp.push(to_cmuc(np.tile(ni, (1, 4)))) dst = cp.dev_matrix_cmf(src.h, src.w) cp.fill(dst, 0) cp.image_move(dst, src, 128, 128, 1, -10, -4) res = cp.pull(dst) #set_trace() plt.matshow(res[0:128**2, 0].reshape(128, 128)) plt.colorbar() plt.show()
def color_test(ni): ts = 128 src = cp.push(to_cmuc(np.tile(ni,(1,4)))) dst = cp.dev_matrix_cmf(ts**2*3,src.w) cp.fill(dst,0) cp.image_move(dst,src,128,ts,4,-10,-4) res = cp.pull(dst) plt.matshow(res[0:ts**2,0].reshape(ts,ts), cmap = plt.cm.bone_r) plt.matshow(res[ts**2:2*ts**2,0].reshape(ts,ts), cmap = plt.cm.bone_r) plt.matshow(res[2*ts**2:3*ts**2,0].reshape(ts,ts), cmap = plt.cm.bone_r) plt.show()
def test_pixel_classes(): w, h = 512, 512 input_channels, pyramid_channels = 4, 3 pic = Image.open("tests/data/lena.bmp").resize((w, h)).convert("RGBA") pic = np.asarray(pic).reshape(h, w * 4) pic_d = cp.push(pic) pyr = cp.dev_image_pyramid_f(pic_d.h / 2, pic_d.w / input_channels / 2, 4, pyramid_channels) pyr.build(pic_d, 4) plt.matshow(pic[0:h:2, 0:4 * w:8]) #plt.matshow(cp.pull(pyr.get(1,0))) #plt.title("Channel0") #plt.matshow(cp.pull(pyr.get(1,1))) #plt.title("Channel1") #plt.matshow(cp.pull(pyr.get(1,2))) #plt.title("Channel2") #plt.matshow(cp.pull(pyr.get_all_channels(1))) #plt.title("allchannels level 1") #plt.show() # create source image from higher level of pyramid pic1 = pyr.get_all_channels(0) for i in xrange(10): smooth(pic1) plt.matshow(cp.pull(pic1)[:h / 2, :w]) ca = cp.dev_cuda_array_f(pic1.h, pic1.w, 1) ca.assign(pic1) # create destination matrix pic0 = pyr.get(0) dst = cp.dev_matrix_rmuc(pic0.h, pic0.w * 4) # uchar4 # generate pixel classes and visualize cp.get_pixel_classes(dst, ca, 1) tmp = cp.pull(dst) tmp = Image.frombuffer("CMYK", (pic0.w, pic0.h), cp.pull(dst).flatten(), "raw", "CMYK", 0, 1).resize( (2 * 512, 2 * 512), Image.NEAREST) tmp.show() print cp.pull(dst) plt.show()
def color_test(ni): ts = 128 src = cp.push(to_cmuc(np.tile(ni, (1, 4)))) dst = cp.dev_matrix_cmf(ts**2 * 3, src.w) cp.fill(dst, 0) cp.image_move(dst, src, 128, ts, 4, -10, -4) res = cp.pull(dst) plt.matshow(res[0:ts**2, 0].reshape(ts, ts), cmap=plt.cm.bone_r) plt.matshow(res[ts**2:2 * ts**2, 0].reshape(ts, ts), cmap=plt.cm.bone_r) plt.matshow(res[2 * ts**2:3 * ts**2, 0].reshape(ts, ts), cmap=plt.cm.bone_r) plt.show()
def test_pixel_classes(): w, h = 512,512 input_channels, pyramid_channels = 4,3 pic = Image.open("tests/data/lena.bmp").resize((w,h)).convert("RGBA") pic = np.asarray(pic).reshape(h,w*4) pic_d = cp.push(pic) pyr = cp.dev_image_pyramid_f(pic_d.h/2,pic_d.w/input_channels/2,4,pyramid_channels) pyr.build(pic_d,4) plt.matshow(pic[0:h:2,0:4*w:8]) #plt.matshow(cp.pull(pyr.get(1,0))) #plt.title("Channel0") #plt.matshow(cp.pull(pyr.get(1,1))) #plt.title("Channel1") #plt.matshow(cp.pull(pyr.get(1,2))) #plt.title("Channel2") #plt.matshow(cp.pull(pyr.get_all_channels(1))) #plt.title("allchannels level 1") #plt.show() # create source image from higher level of pyramid pic1 = pyr.get_all_channels(0) for i in xrange(10): smooth(pic1) plt.matshow(cp.pull(pic1)[:h/2,:w]) ca = cp.dev_cuda_array_f(pic1.h,pic1.w,1) ca.assign(pic1) # create destination matrix pic0 = pyr.get(0) dst = cp.dev_matrix_rmuc(pic0.h,pic0.w*4) # uchar4 # generate pixel classes and visualize cp.get_pixel_classes(dst,ca,1) tmp = cp.pull(dst) tmp = Image.frombuffer("CMYK", (pic0.w,pic0.h), cp.pull(dst).flatten(), "raw", "CMYK", 0, 1 ).resize((2*512,2*512), Image.NEAREST) tmp.show() print cp.pull(dst) plt.show()