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| import argparse |
| import os |
|
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| import numpy as np |
| import json |
| import sys |
| import math |
| import cv2 |
| import glob |
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser(description="convert a dataset from the nsvf paper format to nerf format transforms.json") |
|
|
| parser.add_argument("--aabb_scale", default=1, help="large scene scale factor") |
| parser.add_argument("--white_transparent", action="store_true", help="White is transparent") |
| parser.add_argument("--black_transparent", action="store_true", help="White is transparent") |
| args = parser.parse_args() |
| return args |
|
|
| def variance_of_laplacian(image): |
| return cv2.Laplacian(image, cv2.CV_64F).var() |
|
|
| def sharpness(imagePath): |
| image = cv2.imread(imagePath) |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
| fm = variance_of_laplacian(gray) |
| return fm |
|
|
| if __name__ == "__main__": |
| args = parse_args() |
| AABB_SCALE = int(args.aabb_scale) |
| SKIP_EARLY = 0 |
| IMAGE_FOLDER = "." |
| img_files = [[],[],[]] |
| img_files[0] = sorted(glob.glob(os.path.join(IMAGE_FOLDER, "rgb", f"0_*.png"))) |
| img_files[1] = sorted(glob.glob(os.path.join(IMAGE_FOLDER, "rgb", f"1_*.png"))) |
| img_files[2] = sorted(glob.glob(os.path.join(IMAGE_FOLDER, "rgb", f"2_*.png"))) |
| xx = open("bbox.txt").readline().strip().split(" ") |
| xx = [x for x in xx if x] |
| bbox = tuple(map(float,xx)) |
|
|
| image = cv2.imread(img_files[0][0],cv2.IMREAD_UNCHANGED) |
| w = image.shape[1] |
| h = image.shape[0] |
| if (image.shape[2] == 3 or (image.shape[2] == 4 and image[0][0][3] != 0)): |
| x = w-1 |
| if (image[0][0][0] == 0 and image[0][0][1] == 0 and image[0][0][2] == 0): |
| print("black opaque background detected") |
| args.black_transparent=True |
| elif (image[0][0][0] == 255 and image[0][0][1] == 255 and image[0][0][2] == 255): |
| print("white opaque background detected") |
| args.white_transparent=True |
| elif (image[0][x][0] == 0 and image[0][x][1] == 0 and image[0][x][2] == 0): |
| print("black opaque background detected") |
| args.black_transparent=True |
| elif (image[0][x][0] == 255 and image[0][x][1] == 255 and image[0][x][2] == 255): |
| print("white opaque background detected") |
| args.white_transparent=True |
| else: |
| print("cant detect background") |
| exit() |
| elif (image.shape[2] == 4): |
| print("transparent alpha channel detected, first pixel alpha = ", image[0][0][3]) |
|
|
| lines = map(str.strip,open("intrinsics.txt","r").readlines()) |
| els = tuple(map(float, " ".join(lines).split(" "))) |
| print(els) |
| if len(els) == 11: |
| fl_x = els[0] |
| fl_y = els[0] |
| cx = els[1] |
| cy = els[2] |
| elif len(els) == 16: |
| angle_x=math.pi/2 |
| fl_x = els[0] |
| fl_y = els[5] |
| cx = els[2] |
| cy = els[6] |
| else: |
| print("dont understand intrinsics file", els) |
| exit() |
| |
| angle_x = math.atan(w/(fl_x*2))*2 |
| angle_y = math.atan(h/(fl_y*2))*2 |
| fovx = angle_x*180/math.pi |
| fovy = angle_y*180/math.pi |
| k1 = 0 |
| k2 = 0 |
| p1 = 0 |
| p2 = 0 |
|
|
| print(f"camera:\n\tres={w,h}\n\tcenter={cx,cy}\n\tfocal={fl_x,fl_y}\n\tfov={fovx,fovy}\n\tk={k1,k2} p={p1,p2}") |
| centroid = [(bbox[0]+bbox[3])*0.5,(bbox[1]+bbox[4])*0.5,(bbox[2]+bbox[5])*0.5] |
| print("bbox is ", bbox) |
| print("centroid is ", centroid) |
| radius = [(bbox[3]-bbox[0])*0.5,(bbox[4]-bbox[1])*0.5,(bbox[5]-bbox[2])*0.5] |
| scale = 0.5/np.max(radius) |
| print("radius is ", np.max(radius)) |
|
|
| for itype in [0,1,2]: |
| if (img_files[2]): |
| OUT_PATH = ["transforms_train.json", "transforms_val.json", "transforms_test.json"][itype] |
| else: |
| OUT_PATH = ["transforms_train.json", "transforms_test.json", ""][itype] |
| if OUT_PATH == "": |
| break |
| out = { |
| "camera_angle_x": angle_x, |
| "camera_angle_y": angle_y, |
| "fl_x": fl_x, |
| "fl_y": fl_y, |
| "k1": k1, |
| "k2": k2, |
| "p1": p1, |
| "p2": p2, |
| "cx": cx, |
| "cy": cy, |
| "w": w, |
| "h": h, |
| "scale": 1, |
| "white_transparent": args.white_transparent, |
| "black_transparent": args.black_transparent, |
| "aabb_scale": AABB_SCALE,"frames":[] |
| } |
| for img_f in img_files[itype]: |
| pose_f = os.path.join(IMAGE_FOLDER,"pose",os.path.splitext(os.path.basename(img_f))[0]+".txt") |
| elems = tuple(map(float," ".join(open(pose_f).readlines()).split(" "))) |
| name = img_f |
| m = np.array(elems).reshape(4,4) |
| b = sharpness(name) |
| |
| c2w = m |
| c2w[0:3,3] -= centroid |
| c2w[0:3,3] *= scale |
| |
| c2w[0:3,2] *= -1 |
| c2w[0:3,1] *= -1 |
| c2w = c2w[[0,2,1,3],:] |
| c2w[2,:] *= -1 |
|
|
| frame = {"file_path": name, "sharpness": b, "transform_matrix": c2w} |
| out["frames"].append(frame) |
|
|
| nframes = len(out["frames"]) |
|
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|
|
| for f in out["frames"]: |
| f["transform_matrix"] = f["transform_matrix"].tolist() |
| print(nframes,"frames") |
| print(f"writing {OUT_PATH}...") |
| with open(OUT_PATH, "w") as outfile: |
| json.dump(out, outfile, indent=2) |
|
|
