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@@ -0,0 +1,106 @@
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import os
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import numpy as np
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import cv2
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from pathlib import Path
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from tqdm import tqdm
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import argparse
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import json
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FAST_EXPOSURE_CUTOFF = 0.1 # good for real data (0.1 for synthetic)
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WELL_EXPOSURE_CUTOFF = 0.98 # good for real data (0.9 for synthetic)
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def apply_correction(coefs, img, CORRECTION_CURVE_TYPE) -> np.ndarray:
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# check that img is in range 0-255
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assert img.dtype == np.uint8
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assert CORRECTION_CURVE_TYPE in ['gamma']
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img = ((img.astype(np.float32) / 255.0) ** np.array(coefs).T) # * 255.0
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# img = ((img.astype(np.float32) / 255.0) ** coefs[None, None, :]) * 255.0
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return img
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def cut_weighting_function(pixels, exposures):
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weights = np.zeros(pixels.shape, dtype=np.float32)
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if exposures == WELL_EXPOSURE:
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weights = np.clip(-20.0 * (pixels - WELL_EXPOSURE_CUTOFF) + 1, 0.0, 1.0)
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else:
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weights = np.clip(100.0 * (pixels - FAST_EXPOSURE_CUTOFF), 0.0, 1.0)
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return weights
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os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--well_dir', type=str)
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parser.add_argument('--fast_dir', type=str)
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parser.add_argument('--out_dir', type=str)
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parser.add_argument('--do_linearization', action='store_true')
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parser.add_argument('--experiment_location', type=str, required=True)
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args = parser.parse_args()
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experiment_setup = args.experiment_location + '/capture_settings.json'
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assert os.path.exists(experiment_setup), "!!!! Cannot find the files for exposure setup. !!!"
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with open(experiment_setup) as f:
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data = json.load(f)
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WELL_EXPOSURE = 1.0
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FAST_EXPOSURE = data["right"]["shutter_speed"] / data["left"]["shutter_speed"]
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if not os.path.exists(args.out_dir):
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os.makedirs(args.out_dir)
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well_addr = args.well_dir
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with open(f'linearize-ricoh-z10-wb-3500.json') as f:
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camera_data = json.load(f)
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CORRECTION_CURVE_TYPE = 'gamma'
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well_images = []
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if args.do_linearization:
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for path in Path(well_addr).rglob('*.jpg'):
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well_images.append(path)
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else:
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for path in Path(well_addr).rglob('*.exr'):
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well_images.append(path)
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for well_addr in tqdm(well_images):
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well_addr = str(well_addr)
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hdr_well_image = cv2.imread(well_addr, cv2.IMREAD_UNCHANGED)
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hdr_fast_image = cv2.imread(well_addr.replace(args.well_dir, args.fast_dir), cv2.IMREAD_UNCHANGED)
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# hdr_well_image = np.uint8(hdr_well_image * 255.0)
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# hdr_fast_image = np.uint8(hdr_fast_image * 255.0)
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if args.do_linearization:
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hdr_well_image = apply_correction((camera_data["b"], camera_data["g"], camera_data["r"]), np.array(hdr_well_image), CORRECTION_CURVE_TYPE)
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hdr_fast_image = apply_correction((camera_data["b"], camera_data["g"], camera_data["r"]), np.array(hdr_fast_image), CORRECTION_CURVE_TYPE)
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# well_mask_addr = well_addr.replace('/well_expo/', '/well_mask/')
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# well_mask_addr = well_mask_addr.replace('.exr', '.png')
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# well_mask = cv2.imread(well_mask_addr)
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# well_mask = well_mask.astype(np.float32) / 255.0
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# well_mask[well_mask > 0.1] = 1.0
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weights_well = cut_weighting_function(hdr_well_image, WELL_EXPOSURE)
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weights_fast = cut_weighting_function(hdr_fast_image, FAST_EXPOSURE)
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# fast_mask = cv2.imread(well_mask_addr.replace('/well_mask/', '/fast_mask/'))
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# fast_mask = fast_mask.astype(np.float32) / 255.0
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# final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + weights_fast * (hdr_fast_image / FAST_EXPOSURE)
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# final_output = final_output / (weights_well+weights_well)
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final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + (hdr_fast_image / FAST_EXPOSURE)
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# final_output = weights_well * (hdr_well_image / WELL_EXPOSURE)
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output_addr = well_addr.replace(args.well_dir, args.out_dir)
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if args.do_linearization:
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output_addr = output_addr.replace('jpg', '.exr')
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cv2.imwrite(output_addr, np.float32(final_output) )
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# import pdb; pdb.set_trace()
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@@ -1 +1 @@
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{"B": [54.09578323364258, 0.2747989296913147, 0.0019784104079008102], "G": [48.520992279052734, 0.05255260691046715, 0.0029351452831178904], "R": [47.87550735473633, 0.19775021076202393, 0.0023990024346858263], "type": "deg2"}
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{"b": [2.6464749792362516], "g": [3.2232453214278767], "r": [2.9613987706773113]}
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@@ -1 +0,0 @@
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{"B": [46.82997512817383, 0.11160952597856522, 0.002294188365340233], "G": [41.67820739746094, 0.09581712633371353, 0.0028952741995453835], "R": [37.4744873046875, 0.22809027135372162, 0.002323441905900836], "type": "deg2"}
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@@ -0,0 +1,58 @@
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"""
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Script to render IBL renders
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Usage: blender <blend file> -P hdr_blender.py -b
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"""
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import os
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import random
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from math import radians
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from os import listdir
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from os.path import isfile, join
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import bpy
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import cycles
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from mathutils import Euler, Matrix, Vector
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import sys
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argv = sys.argv
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argv = argv[argv.index("--") + 1:] # get all args after "--"
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inp_dirs = [argv[0]]
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out_dirs = [argv[1]]
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pairs = list(zip(inp_dirs,out_dirs))
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scn = bpy.context.scene
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scn.render.engine = 'CYCLES'
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scn.render.image_settings.file_format = 'OPEN_EXR'
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scn.world.use_nodes = True
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# Enabf_le GPU rendering.
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# bpy.context.preferences.addons['cycles'].preferences.compute_device_type = 'CUDA'
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bpy.context.preferences.addons['cycles'].preferences.get_devices()
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for device in bpy.context.preferences.addons['cycles'].preferences.devices:
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if "TITAN RX" == device.name:
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device.use = True
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else:
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device.use = False
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# select world node tree
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wd = scn.world
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nt = bpy.data.worlds[wd.name].node_tree
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for pair in pairs:
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inp, out = pair
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inp = inp
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os.makedirs(out,exist_ok=True)
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S = bpy.context.scene
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envFiles = [f for f in listdir(inp) if isfile(join(inp, f)) and f.endswith('.exr')]
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i = 0
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for env_map in envFiles:
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env_file = join(inp, env_map)
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bpy.ops.image.open(filepath=env_file, directory=inp, files=[
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{"name": env_map, "name": env_map}], show_multiview=False)
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output_name = env_map.split('.')[0] +'.exr'
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backNode = nt.nodes['Environment Texture']
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backNode.image = bpy.data.images.load(env_file)
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bpy.context.scene.render.filepath = os.path.join(out, output_name)
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print("Output",os.path.join(out, output_name))
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bpy.ops.render.render(write_still=True)
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@@ -0,0 +1,160 @@
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export scene_name=my_apartment_ns
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export output_dir=renders/${scene_name}/lantern-nerfacto/video_images
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export root_dir=data/${scene_name}
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export model_path=outputs/${scene_name}/lantern-nerfacto/2024-05-16_212421/config.yml
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# camera paths
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# video ground: data/Henry_Gagnon_blue_et_rouge_ns/camera_paths/2024-05-16_015020.json
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# still bench: 2024-05-16_015020_render
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# pespective cam: 2024-05-16_015020_render3
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# meeting room
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# export camera_path_name=2024-05-16_201826_persp
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# export camera_path_name=2024-05-16_201826_envmap
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# my_apartment
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# export camera_path_name=2024-05-16_212421_persp
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# export camera_path_name=2024-05-16_212421_env1
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# export camera_path_name=2024-05-16_212421_env2
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# export camera_path_name=2024-05-16_212421_env3
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# export camera_path_name=2024-05-16_212421_env4
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export camera_path_name=2024-05-16_212421_env5
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/well-expo/ --output-format images
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/fast-expo/ --output-format images --rendered_output_names rgb_fast
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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# blender --background video_rendering_code/scene_new.blend -P video_rendering_code/hdr_blender.py -- $output_dir/${camera_path_name}/combined/ $output_dir/${camera_path_name}/render/
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#########
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export camera_path_name=2024-05-16_212421_env1
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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export camera_path_name=2024-05-16_212421_env2
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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export camera_path_name=2024-05-16_212421_env3
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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export camera_path_name=2024-05-16_212421_env4
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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export camera_path_name=2024-05-16_212421_env5
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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echo "Combine two exposures"
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python video_rendering_code/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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####
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# meeting room teaser
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export scene_name=meeting_room_ns
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export output_dir=renders/${scene_name}/lantern-nerfacto/video_images
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export root_dir=data/${scene_name}
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export model_path=outputs/${scene_name}/lantern-nerfacto/2024-05-16_201826/config.yml
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# meeting room
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# export camera_path_name=2024-05-16_201826_persp
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# export camera_path_name=2024-05-16_201826_persp2
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#export camera_path_name=2024-05-16_201826_env1
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# export camera_path_name=2024-05-16_201826_env2
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# export camera_path_name=2024-05-16_201826_env3
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# export camera_path_name=2024-05-16_201826_env4
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# export camera_path_name=2024-05-16_201826_env5
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# export camera_path_name=2024-05-16_201826_env6
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# export camera_path_name=2024-05-16_201826_env7
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export camera_path_name=2024-05-16_201826_env8
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/well-expo/ --output-format images
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/fast-expo/ --output-format images --rendered_output_names rgb_fast
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echo "Combine two exposures"
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python lantern_scripts/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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# Fig. 5 - ABO objects
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export scene_name=LVSN_LAB_OpenSFM
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export output_dir=renders/${scene_name}/lantern-nerfacto/video_images
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export root_dir=data/${scene_name}
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export model_path=outputs/${scene_name}/lantern-nerfacto/step_2/config.yml
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# export camera_path_name=persp1
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export camera_path_name=persp2
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# export camera_path_name=env1
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# export camera_path_name=env2
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# export camera_path_name=env3
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/well-expo/ --output-format images
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/fast-expo/ --output-format images --rendered_output_names rgb_fast
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echo "Combine two exposures"
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python lantern_scripts/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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######### Render videos ############
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# LVSN lab upstairs
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export scene_name=LVSN_LAB_OpenSFM
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export model_type=hdr-nerfacto
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# export model_path=outputs/${scene_name}/lantern-nerfacto/step_2/config.yml
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export output_dir=renders/${scene_name}/${model_type}/video_images
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export model_path=outputs/${scene_name}/${model_type}/2024-05-18_143132/config.yml
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export camera_path_name=video3
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export camera_path=data/${scene_name}/camera_paths/${camera_path_name}.json
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/well-expo/ --output-format images
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ns-render camera-path --load-config $model_path --camera-path-filename $camera_path --output-path $output_dir/${camera_path_name}/fast-expo/ --output-format images --rendered_output_names rgb_fast
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echo "Combine two exposures"
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python lantern_scripts/combine.py --well_dir $output_dir/${camera_path_name}/well-expo/ --fast_dir $output_dir/${camera_path_name}/fast-expo/ --out_dir $output_dir/${camera_path_name}/combined/ --do_linearization --experiment_location $root_dir
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# for Yannick
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zip -rq combined_${scene_name}_${model_type}.zip $output_dir/${camera_path_name}/combined
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blender --background video_rendering_code/scene_new.blend -P video_rendering_code/hdr_blender.py -- $output_dir/${camera_path_name}/combined/ $output_dir/${camera_path_name}/render/
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mkdir $output_dir/${camera_path_name}/render_tonemapped
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python lantern_scripts/tonemap.py --data_dir $output_dir/${camera_path_name}/render/ --out_dir $output_dir/${camera_path_name}/render_tonemapped
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python lantern_scripts/mp4_maker.py --input_Addr $output_dir/${camera_path_name}/render_tonemapped --out_file $output_dir/${camera_path_name}/video_render.mp4
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##
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export scene_name=meeting_room_ns
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export model_path=outputs/${scene_name}/lantern-nerfacto/2024-05-16_201826/config.yml
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