HParams 代替方案+ ocv 鼠标选框 + UV 展开

当前常用的脚本 HParams 不使 tf 的方法opencv 鼠标选框Torch 默认模型位置UV 展开，meshplot 使用

HParams 不使 tf 的方法

很多th的代码，就简单用了 Hparams 就 import tf，可以如下代替

from easydict import EasyDict as edictdef HParams(**args): return edict(args)# 照常定义hparams = HParams( rescaling_max=0.9, use_lws=False, n_fft=800, hop_size=200, win_size=800, sample_rate=16000,)

下面是一些扩充

def hparams_debug_string(): values = hparams hp = [" %s: %s" % (name, values[name]) for name in sorted(hparams.keys()) if name != "sentences"] return "Hyperparameters:n" + "n".join(hp)def create_hparams(hparams_string=None): if hparams_string: print(hparams_string) arr=hparams_string.split(";") for e in arr: k, v = [ x.strip() for x in e.split("=") ] _v = getattr(hparams, k, "") setattr(hparams, k, type(_v)(v))import argparseparser.add_argument('--hparams', type=str, help='comma separated name=value pairs')args = parser.parse_args()create_hparams(args.hparams) opencv 鼠标选框 b=64w=h=640img=np.zeros( (w, h, 3), np.uint8 )for i in range(h//b): for j in range(w//b): np.random.randint(0, 256, 3) #.tolist() img[ i*b:(i+1)*b, j*b:(j+1)*b ] = c[None, None, :]r = cv2.selectROI("Image", img, False, False)print(r) Torch 默认模型位置 import osos.environ["TORCH_HOME"]="xxxx/torch" # models must in "hub/checkpoints"from torchvision import modelsfeatures = models.vgg16(pretrained=True).features UV 展开，meshplot 使用

meshplot tutorial
meshplot exp
igl

import meshplotmeshplot.offline()from meshplot import plot, subplot, interactv, f = igl.read_triangle_mesh("mouse_for_uv.obj")# Fix two points on the boundaryb = np.array([2, 1])bnd = igl.boundary_loop(f)b[0] = bnd[0]b[1] = bnd[int(bnd.size / 2)]bc = np.array([[0.0, 0.0], [1.0, 0.0]])# LSCM parametrization_, uv = igl.lscm(v, f, b, bc)#print(uv.shape, type(uv))#np.save("data/uv.npy", uv)p = subplot(v, f, uv=uv, shading={"wireframe": False, "flat": False}, s=[1, 2, 0])subplot(uv, f, uv=uv, shading={"wireframe": False, "flat": False}, s=[1, 2, 1], data=p)p.save("test.html")

然后打开 html 就可以看了。

这种不方便，有时间介绍另一种。