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| import numpy as np | |
| from numpy import sin, cos | |
| from math import pi as π | |
| from my3d import camera_pose | |
| from my.config import BaseConf | |
| import random | |
| def get_K(H, W, FoV_x): | |
| FoV_x = FoV_x / 180 * π # to rad | |
| f = 1 / np.tan(FoV_x / 2) * (W / 2) | |
| K = np.array([ | |
| [f, 0, -(W/2 - 0.5)], | |
| [0, -f, -(H/2 - 0.5)], | |
| [0, 0, -1] | |
| ]) | |
| return K | |
| SIDEVIEW_PROMPTS = [ | |
| "front view of", "side view of", "backside view of", "side view of" | |
| ] | |
| TOPVIEW_PROMPT = "overhead view of" | |
| def train_eye_with_prompts(r, n): | |
| hs = np.random.rand(n) * 360 | |
| vs = np.random.rand(n) * np.deg2rad(100) | |
| vs = np.clip(vs, 1e-2, π-1e-2) | |
| prompts = [] | |
| v_thresh = np.deg2rad(30) | |
| for i in range(n): | |
| _p = "" | |
| if vs[i] < v_thresh: | |
| _p = TOPVIEW_PROMPT | |
| else: | |
| _a = hs[i] | |
| _a = (_a + 45) % 360 | |
| _quad = int(_a // 90) | |
| _p = SIDEVIEW_PROMPTS[_quad] | |
| prompts.append(_p) | |
| θ = np.deg2rad(hs) | |
| # φ = v | |
| φ = np.arccos(1 - 2 * (vs / π)) | |
| eyes = np.zeros((n, 3)) | |
| eyes[:, 0] = r * sin(φ) * cos(π-θ) # x | |
| eyes[:, 2] = r * sin(φ) * sin(π-θ) # z | |
| eyes[:, 1] = r * cos(φ) # y | |
| return eyes, prompts | |
| def spiral_poses( | |
| radius, height, | |
| num_steps=20, num_rounds=1, | |
| center=np.array([0, 0, 0]), up=np.array([0, 1, 0]), | |
| ): | |
| eyes = [] | |
| for i in range(num_steps): | |
| ratio = (i + 1) / num_steps | |
| Δy = height * (1 - ratio) | |
| θ = ratio * (360 * num_rounds) | |
| θ = θ / 180 * π | |
| # _r = max(radius * ratio, 0.5) | |
| _r = max(radius * sin(ratio * π / 2), 0.5) | |
| Δx, Δz = _r * np.array([np.cos(θ), np.sin(θ)]) | |
| eyes.append(center + [Δx, Δy, Δz]) | |
| poses = [ | |
| camera_pose(e, center - e, up) for e in eyes | |
| ] | |
| return poses | |
| class PoseConfig(BaseConf): | |
| rend_hw: int = 64 | |
| FoV: float = 60.0 | |
| R: float = 1.5 | |
| def make(self): | |
| cfgs = self.dict() | |
| hw = cfgs.pop("rend_hw") | |
| cfgs["H"] = hw | |
| cfgs["W"] = hw | |
| return Poser(**cfgs) | |
| class Poser(): | |
| def __init__(self, H, W, FoV, R): | |
| self.H, self.W = H, W | |
| self.R = R | |
| self.K = get_K(H, W, FoV) | |
| def sample_train(self, n): | |
| eyes, prompts = train_eye_with_prompts(r=self.R, n=n) | |
| up = np.array([0, 1, 0]) | |
| poses = [ | |
| camera_pose(e, -e, up) for e in eyes | |
| ] | |
| poses = np.stack(poses, 0) | |
| # FoV during training: [40,70] | |
| random_Ks = [ | |
| get_K(self.H, self.W, random.random() * 30 + 40) | |
| for i in range(len(poses)) | |
| # self.K for i in range(len(poses)) | |
| ] | |
| # return self.K, poses, prompts | |
| return random_Ks, poses, prompts | |
| def sample_test(self, n): | |
| poses = spiral_poses(self.R, self.R, n, num_rounds=3) | |
| poses = np.stack(poses, axis=0) | |
| return self.K, poses | |