| """Render a FLAME mesh overlay with a separately obtained FLAME model's topology.""" |
| import argparse |
| import pickle |
| from pathlib import Path |
|
|
| import numpy as np |
| from PIL import Image, ImageDraw |
|
|
| from load_sample import load_sample |
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|
|
| def visualize(sample, faces, output): |
| camera = sample["camera"] |
| vertices = sample["vertices_world"] @ np.asarray(camera["R"]).T + np.asarray(camera["t"]) |
| K = np.asarray(camera["K"]) |
| projection = vertices @ K.T |
| assert (vertices[:, 2] > 0).all() |
| uv = projection[:, :2] / projection[:, 2:] |
| image = Image.fromarray(sample["image"]) |
| width, height = image.size |
| assert [width, height] == [camera["width"], camera["height"]] |
| |
| mesh = Image.new("RGB", image.size, (0, 0, 0)) |
| mask = Image.new("L", image.size, 0) |
| draw, draw_mask = ImageDraw.Draw(mesh), ImageDraw.Draw(mask) |
| triangles = vertices[faces] |
| normals = np.cross(triangles[:, 1] - triangles[:, 0], triangles[:, 2] - triangles[:, 0]) |
| normals /= np.maximum(np.linalg.norm(normals, axis=1, keepdims=True), 1e-12) |
| colors = ((normals * 0.5 + 0.5) * 255).astype(np.uint8) |
| for index in np.argsort(triangles[..., 2].mean(axis=1))[::-1]: |
| points = [tuple(p) for p in uv[faces[index]].tolist()] |
| draw.polygon(points, fill=tuple(colors[index].tolist())) |
| draw_mask.polygon(points, fill=255) |
| overlay = Image.composite(Image.blend(image, mesh, 0.55), image, mask) |
| x0, y0, x1, y1 = sample["metadata"]["bbox_xyxy"] |
| ImageDraw.Draw(overlay).rectangle((x0, y0, x1, y1), outline=(255, 180, 0), width=2) |
| segmentation = Image.fromarray(np.repeat((sample["foreground"] * 255).astype(np.uint8)[..., None], 3, axis=2)) |
| result = Image.new("RGB", (width * 3, height)) |
| for index, panel in enumerate((image, overlay, segmentation)): |
| result.paste(panel, (index * width, 0)) |
| result.thumbnail((1500, 900)) |
| result.save(output) |
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|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--root", type=Path, required=True) |
| parser.add_argument("--subject", required=True) |
| parser.add_argument("--frame", required=True) |
| parser.add_argument("--camera", required=True) |
| parser.add_argument("--flame-model", type=Path, required=True) |
| parser.add_argument("--output", type=Path, required=True) |
| args = parser.parse_args() |
| with args.flame_model.open("rb") as stream: |
| faces = np.asarray(pickle.load(stream, encoding="latin1")["f"], dtype=np.int32) |
| visualize(load_sample(args.root, args.subject, args.frame, args.camera), faces, args.output) |
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|
