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Merge pull request #1357 from FlorisE/master
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colmap2nerf: Use separate intrinsics if available
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@Tom94
Tom94 committed Jun 6, 2023
2 parents e45134b + eb14398 commit b8c66f4
Showing 1 changed file with 98 additions and 81 deletions.
179 changes: 98 additions & 81 deletions scripts/colmap2nerf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,104 +202,119 @@ def closest_point_2_lines(oa, da, ob, db): # returns point closest to both rays
TEXT_FOLDER = args.text
OUT_PATH = args.out
print(f"outputting to {OUT_PATH}...")
cameras = {}
with open(os.path.join(TEXT_FOLDER,"cameras.txt"), "r") as f:
angle_x = math.pi / 2
camera_angle_x = math.pi / 2
for line in f:
# 1 SIMPLE_RADIAL 2048 1536 1580.46 1024 768 0.0045691
# 1 OPENCV 3840 2160 3178.27 3182.09 1920 1080 0.159668 -0.231286 -0.00123982 0.00272224
# 1 RADIAL 1920 1080 1665.1 960 540 0.0672856 -0.0761443
if line[0] == "#":
continue
els = line.split(" ")
w = float(els[2])
h = float(els[3])
fl_x = float(els[4])
fl_y = float(els[4])
k1 = 0
k2 = 0
k3 = 0
k4 = 0
p1 = 0
p2 = 0
cx = w / 2
cy = h / 2
is_fisheye = False
camera = {}
camera_id = int(els[0])
camera["w"] = float(els[2])
camera["h"] = float(els[3])
camera["fl_x"] = float(els[4])
camera["fl_y"] = float(els[4])
camera["k1"] = 0
camera["k2"] = 0
camera["k3"] = 0
camera["k4"] = 0
camera["p1"] = 0
camera["p2"] = 0
camera["cx"] = camera["w"] / 2
camera["cy"] = camera["h"] / 2
camera["is_fisheye"] = False
if els[1] == "SIMPLE_PINHOLE":
cx = float(els[5])
cy = float(els[6])
camera["cx"] = float(els[5])
camera["cy"] = float(els[6])
elif els[1] == "PINHOLE":
fl_y = float(els[5])
cx = float(els[6])
cy = float(els[7])
camera["fl_y"] = float(els[5])
camera["cx"] = float(els[6])
camera["cy"] = float(els[7])
elif els[1] == "SIMPLE_RADIAL":
cx = float(els[5])
cy = float(els[6])
k1 = float(els[7])
camera["cx"] = float(els[5])
camera["cy"] = float(els[6])
camera["k1"] = float(els[7])
elif els[1] == "RADIAL":
cx = float(els[5])
cy = float(els[6])
k1 = float(els[7])
k2 = float(els[8])
camera["cx"] = float(els[5])
camera["cy"] = float(els[6])
camera["k1"] = float(els[7])
camera["k2"] = float(els[8])
elif els[1] == "OPENCV":
fl_y = float(els[5])
cx = float(els[6])
cy = float(els[7])
k1 = float(els[8])
k2 = float(els[9])
p1 = float(els[10])
p2 = float(els[11])
camera["fl_y"] = float(els[5])
camera["cx"] = float(els[6])
camera["cy"] = float(els[7])
camera["k1"] = float(els[8])
camera["k2"] = float(els[9])
camera["p1"] = float(els[10])
camera["p2"] = float(els[11])
elif els[1] == "SIMPLE_RADIAL_FISHEYE":
is_fisheye = True
cx = float(els[5])
cy = float(els[6])
k1 = float(els[7])
camera["is_fisheye"] = True
camera["cx"] = float(els[5])
camera["cy"] = float(els[6])
camera["k1"] = float(els[7])
elif els[1] == "RADIAL_FISHEYE":
is_fisheye = True
cx = float(els[5])
cy = float(els[6])
k1 = float(els[7])
k2 = float(els[8])
camera["is_fisheye"] = True
camera["cx"] = float(els[5])
camera["cy"] = float(els[6])
camera["k1"] = float(els[7])
camera["k2"] = float(els[8])
elif els[1] == "OPENCV_FISHEYE":
is_fisheye = True
fl_y = float(els[5])
cx = float(els[6])
cy = float(els[7])
k1 = float(els[8])
k2 = float(els[9])
k3 = float(els[10])
k4 = float(els[11])
camera["is_fisheye"] = True
camera["fl_y"] = float(els[5])
camera["cx"] = float(els[6])
camera["cy"] = float(els[7])
camera["k1"] = float(els[8])
camera["k2"] = float(els[9])
camera["k3"] = float(els[10])
camera["k4"] = float(els[11])
else:
print("Unknown camera model ", els[1])
# fl = 0.5 * w / tan(0.5 * angle_x);
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
camera["camera_angle_x"] = math.atan(camera["w"] / (camera["fl_x"] * 2)) * 2
camera["camera_angle_y"] = math.atan(camera["h"] / (camera["fl_y"] * 2)) * 2
camera["fovx"] = camera["camera_angle_x"] * 180 / math.pi
camera["fovy"] = camera["camera_angle_y"] * 180 / math.pi

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} ")
print(f"camera {camera_id}:\n\tres={camera['w'],camera['h']}\n\tcenter={camera['cx'],camera['cy']}\n\tfocal={camera['fl_x'],camera['fl_y']}\n\tfov={camera['fovx'],camera['fovy']}\n\tk={camera['k1'],camera['k2']} p={camera['p1'],camera['p2']} ")
cameras[camera_id] = camera

if len(cameras) == 0:
print("No cameras found!")
sys.exit(1)

with open(os.path.join(TEXT_FOLDER,"images.txt"), "r") as f:
i = 0
bottom = np.array([0.0, 0.0, 0.0, 1.0]).reshape([1, 4])
out = {
"camera_angle_x": angle_x,
"camera_angle_y": angle_y,
"fl_x": fl_x,
"fl_y": fl_y,
"k1": k1,
"k2": k2,
"k3": k3,
"k4": k4,
"p1": p1,
"p2": p2,
"is_fisheye": is_fisheye,
"cx": cx,
"cy": cy,
"w": w,
"h": h,
"aabb_scale": AABB_SCALE,
"frames": [],
}
if len(cameras) == 1:
camera = cameras[camera_id]
out = {
"camera_angle_x": camera["camera_angle_x"],
"camera_angle_y": camera["camera_angle_y"],
"fl_x": camera["fl_x"],
"fl_y": camera["fl_y"],
"k1": camera["k1"],
"k2": camera["k2"],
"k3": camera["k3"],
"k4": camera["k4"],
"p1": camera["p1"],
"p2": camera["p2"],
"is_fisheye": camera["is_fisheye"],
"cx": camera["cx"],
"cy": camera["cy"],
"w": camera["w"],
"h": camera["h"],
"aabb_scale": AABB_SCALE,
"frames": [],
}
else:
out = {
"frames": [],
"aabb_scale": AABB_SCALE
}

up = np.zeros(3)
for line in f:
Expand Down Expand Up @@ -333,6 +348,8 @@ def closest_point_2_lines(oa, da, ob, db): # returns point closest to both rays
up += c2w[0:3,1]

frame = {"file_path":name,"sharpness":b,"transform_matrix": c2w}
if len(cameras) != 1:
frame.update(cameras[int(elems[8])])
out["frames"].append(frame)
nframes = len(out["frames"])

Expand Down Expand Up @@ -425,16 +442,16 @@ def closest_point_2_lines(oa, da, ob, db): # returns point closest to both rays
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

for frame in out['frames']:
img = cv2.imread(frame['file_path'])
for frame in out["frames"]:
img = cv2.imread(frame["file_path"])
outputs = predictor(img)

output_mask = np.zeros((img.shape[0], img.shape[1]))
for i in range(len(outputs['instances'])):
if outputs['instances'][i].pred_classes.cpu().numpy()[0] in mask_ids:
pred_mask = outputs['instances'][i].pred_masks.cpu().numpy()[0]
for i in range(len(outputs["instances"])):
if outputs["instances"][i].pred_classes.cpu().numpy()[0] in mask_ids:
pred_mask = outputs["instances"][i].pred_masks.cpu().numpy()[0]
output_mask = np.logical_or(output_mask, pred_mask)

rgb_path = Path(frame['file_path'])
mask_name = str(rgb_path.parents[0] / Path('dynamic_mask_' + rgb_path.name.replace('.jpg', '.png')))
rgb_path = Path(frame["file_path"])
mask_name = str(rgb_path.parents[0] / Path("dynamic_mask_" + rgb_path.name.replace(".jpg", ".png")))
cv2.imwrite(mask_name, (output_mask*255).astype(np.uint8))

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