diff --git a/lantern_scripts/combine.py b/lantern_scripts/combine.py
index 0ae17ed2a..7c2035113 100755
--- a/lantern_scripts/combine.py
+++ b/lantern_scripts/combine.py
@@ -7,7 +7,7 @@ import argparse
 import json
 
 
-FAST_EXPOSURE_CUTOFF = 0.2 # good for real data (0.1 for synthetic)
+FAST_EXPOSURE_CUTOFF = 0.1 # good for real data (0.1 for synthetic)
 WELL_EXPOSURE_CUTOFF = 0.98 # good for real data (0.9 for synthetic)
 
 
@@ -94,8 +94,9 @@ if __name__ == "__main__":
         # fast_mask = cv2.imread(well_mask_addr.replace('/well_mask/', '/fast_mask/'))
         # fast_mask = fast_mask.astype(np.float32) / 255.0
 
-        final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + weights_fast * (hdr_fast_image / FAST_EXPOSURE)
-        # final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + (hdr_fast_image / FAST_EXPOSURE)
+        # final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + weights_fast * (hdr_fast_image / FAST_EXPOSURE)
+        # final_output = final_output / (weights_well+weights_well)
+        final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + (hdr_fast_image / FAST_EXPOSURE)
         # final_output = weights_well * (hdr_well_image / WELL_EXPOSURE)
 
         output_addr = well_addr.replace(args.well_dir, args.out_dir)
diff --git a/lantern_scripts/combine_well_at_fast.py b/lantern_scripts/combine_well_at_fast.py
new file mode 100755
index 000000000..943d3c028
--- /dev/null
+++ b/lantern_scripts/combine_well_at_fast.py
@@ -0,0 +1,110 @@
+import os
+import numpy as np
+import cv2
+from pathlib import Path
+from tqdm import tqdm
+import argparse
+import json
+
+
+FAST_EXPOSURE_CUTOFF = 0.01 # good for real data (0.1 for synthetic)
+WELL_EXPOSURE_CUTOFF = 0.98 # good for real data (0.9 for synthetic)
+
+
+def apply_correction(coefs, img, CORRECTION_CURVE_TYPE) -> np.ndarray:
+    # check that img is in range 0-255
+    assert img.dtype == np.uint8
+    assert CORRECTION_CURVE_TYPE in ['gamma']
+    img = ((img.astype(np.float32) / 255.0) ** np.array(coefs).T) # * 255.0
+    # img = ((img.astype(np.float32) / 255.0) ** coefs[None, None, :]) * 255.0
+    return img
+
+def cut_weighting_function(pixels, exposures):
+    weights = np.zeros(pixels.shape, dtype=np.float32)
+    
+    if exposures == WELL_EXPOSURE:
+        weights = np.clip(-20.0 * (pixels - WELL_EXPOSURE_CUTOFF) + 1, 0.0, 1.0)
+    else:
+        weights = np.clip(100.0 * (pixels - FAST_EXPOSURE_CUTOFF), 0.0, 1.0)
+
+    return weights
+
+
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--well_dir', type=str, default='/mnt/workspace/lantern/nerfstudio-HDR/well_at_fast/')
+    parser.add_argument('--fast_dir', type=str, default='/mnt/data/nerfstudio_ds/real_data/meeting_room_openSFM/meeting_room_ns/images/')
+    parser.add_argument('--out_dir', type=str, default='./output_well_at_fast/')
+    parser.add_argument('--do_linearization', action='store_true')
+    parser.add_argument('--experiment_location', type=str, default='/mnt/data/nerfstudio_ds/real_data/meeting_room_openSFM')
+    args = parser.parse_args()
+
+    experiment_setup = args.experiment_location + '/capture_settings.json'
+    assert os.path.exists(experiment_setup), "!!!! Cannot find the files for exposure setup.  !!!"
+    with open(experiment_setup) as f:
+        data = json.load(f)
+        WELL_EXPOSURE = 1.0
+        FAST_EXPOSURE = data["right"]["shutter_speed"] / data["left"]["shutter_speed"]
+
+    if not os.path.exists(args.out_dir):
+        os.makedirs(args.out_dir)
+
+    well_addr = args.well_dir
+
+    with open(f'linearize-ricoh-z10-wb-3500.json') as f:
+        camera_data = json.load(f)
+        CORRECTION_CURVE_TYPE = 'gamma'
+
+    well_images = []
+    if args.do_linearization:
+        for path in Path(well_addr).rglob('*-img.png'):
+            well_images.append(path)
+    else:
+        for path in Path(well_addr).rglob('*.exr'):
+            well_images.append(path)
+
+    for well_addr in tqdm(well_images):
+
+        well_addr = str(well_addr)
+
+        print("well_addr: ", well_addr)
+
+        hdr_well_image = cv2.imread(well_addr,  cv2.IMREAD_UNCHANGED)
+        fast_addr = well_addr.replace(args.well_dir, args.fast_dir)
+        fast_addr = fast_addr.replace("-img.png", ".png")
+        hdr_fast_image = cv2.imread(fast_addr, cv2.IMREAD_UNCHANGED)
+
+
+
+        # hdr_well_image = np.uint8(hdr_well_image * 255.0)
+        # hdr_fast_image = np.uint8(hdr_fast_image * 255.0)
+        
+        if args.do_linearization:
+            hdr_well_image = apply_correction((camera_data["b"], camera_data["g"], camera_data["r"]), np.array(hdr_well_image), CORRECTION_CURVE_TYPE)
+            hdr_fast_image = apply_correction((camera_data["b"], camera_data["g"], camera_data["r"]), np.array(hdr_fast_image), CORRECTION_CURVE_TYPE)
+
+
+        # well_mask_addr = well_addr.replace('/well_expo/', '/well_mask/')
+        # well_mask_addr = well_mask_addr.replace('.exr', '.png')
+        # well_mask = cv2.imread(well_mask_addr)
+        # well_mask = well_mask.astype(np.float32) / 255.0
+        # well_mask[well_mask > 0.1] = 1.0
+
+        weights_well = cut_weighting_function(hdr_well_image, WELL_EXPOSURE)
+        weights_fast = cut_weighting_function(hdr_fast_image, FAST_EXPOSURE)
+
+        # fast_mask = cv2.imread(well_mask_addr.replace('/well_mask/', '/fast_mask/'))
+        # fast_mask = fast_mask.astype(np.float32) / 255.0
+
+        # final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + weights_fast * (hdr_fast_image / FAST_EXPOSURE)
+        final_output = weights_well * (hdr_well_image / WELL_EXPOSURE) + (hdr_fast_image / FAST_EXPOSURE)
+        # final_output = weights_well * (hdr_well_image / WELL_EXPOSURE)
+
+        output_addr = well_addr.replace(args.well_dir, args.out_dir)
+        if args.do_linearization:
+            output_addr = output_addr.replace('-img.png', '.exr')
+        cv2.imwrite(output_addr, np.float32(final_output) )
+        # import pdb; pdb.set_trace()
\ No newline at end of file
diff --git a/lantern_scripts/ldr_res.py b/lantern_scripts/ldr_res.py
old mode 100644
new mode 100755
diff --git a/lantern_scripts/linearize_jpgs.py b/lantern_scripts/linearize_jpgs.py
old mode 100644
new mode 100755
index 04927e916..797395a48
--- a/lantern_scripts/linearize_jpgs.py
+++ b/lantern_scripts/linearize_jpgs.py
@@ -25,6 +25,9 @@ if __name__ == "__main__":
     args = parser.parse_args()
     well_addr = args.data_dir
 
+    if not os.path.exists(args.out_dir):
+        os.makedirs(args.out_dir)
+
     with open(f'./linearize-ricoh-z10-wb-3500.json') as f:
         camera_data = json.load(f)
         CORRECTION_CURVE_TYPE = 'gamma'
diff --git a/lantern_scripts/masking_panos.py b/lantern_scripts/masking_panos.py
new file mode 100755
index 000000000..ca9d4c2aa
--- /dev/null
+++ b/lantern_scripts/masking_panos.py
@@ -0,0 +1,38 @@
+import argparse
+import numpy as np
+import os
+from tqdm import tqdm
+from PIL import Image
+from pathlib import Path
+import PIL.ExifTags
+import json
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2
+import warnings 
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_dir', type=str, default='/Users/momo/Desktop/Pandora-HDR_NeRF/final_results/meeting_room/GT/pano/')
+    parser.add_argument('--mask_dir', type=str, default='/Users/momo/Desktop/Pandora-HDR_NeRF/final_results/meeting_room/GT/mask/')
+    parser.add_argument('--out_dir', type=str, default='/Users/momo/Desktop/Pandora-HDR_NeRF/final_results/meeting_room/GT/masked_pano/')
+    args = parser.parse_args()
+    
+    test_images = []
+    for path in Path(args.data_dir).rglob('*.exr'):
+        test_images.append(path)
+
+    for pano_addr in tqdm(test_images):
+        img = cv2.imread(str(pano_addr), cv2.IMREAD_UNCHANGED)
+
+        mask_addr = str(pano_addr).replace(args.data_dir, args.mask_dir)
+        mask_addr = mask_addr.replace('.exr', '.png')
+        mask = cv2.imread(mask_addr, cv2.IMREAD_UNCHANGED)
+        mask = np.float32(mask) / 255.
+
+        # import pdb; pdb.set_trace()
+        masked_img = np.expand_dims(mask, axis=-1) * img
+        # import pdb; pdb.set_trace()
+        out_addr = str(pano_addr).replace(args.data_dir, args.out_dir)
+        # import pdb; pdb.set_trace()
+        cv2.imwrite(out_addr, np.float32(masked_img))
\ No newline at end of file
diff --git a/lantern_scripts/mp4_maker.py b/lantern_scripts/mp4_maker.py
new file mode 100755
index 000000000..1403c6c0e
--- /dev/null
+++ b/lantern_scripts/mp4_maker.py
@@ -0,0 +1,38 @@
+import cv2
+import numpy as np
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Make a GIF from input images')
+    parser.add_argument("--input_Addr", type=str, help='input folder')
+    # parser.add_argument("--input_pano_Addr", type=str, help='input folder')
+    args = parser.parse_args()
+
+    images = []
+
+    from os import listdir
+    from os.path import isfile, join
+
+    img_files = [join(args.input_Addr, f) for f in listdir(args.input_Addr) if isfile(join(args.input_Addr, f))]
+    # pano_files = [join(args.input_pano_Addr, f) for f in listdir(args.input_pano_Addr) if isfile(join(args.input_pano_Addr, f))]
+    img_files = sorted(img_files)
+
+    for i in  range(len(img_files)):
+        img_add = img_files[i]
+        # pano_add = pano_files[i]
+        print(img_add)
+        # print(pano_add)
+        img = cv2.imread(img_add)
+        height, width, layers = img.shape
+        size = (width, height)
+        # pano = cv2.imread(pano_add)
+        # small_pano = cv2.resize(pano, (0, 0), fx=0.3, fy=0.3)
+        # img[0:small_pano.shape[0], 0:small_pano.shape[1]] = small_pano
+        images.append(img)
+        
+    out = cv2.VideoWriter('saussan_ldr_light.mp4', cv2.VideoWriter_fourcc(*'FMP4'), 10, size)
+    # out = cv2.VideoWriter('my_apartement_render.mp4', cv2.VideoWriter_fourcc('X','V','I','D'), 15, size)
+
+    for i in range(len(images)):
+        out.write(images[i])
+    out.release()
\ No newline at end of file
diff --git a/lantern_scripts/rectify_pano.py b/lantern_scripts/rectify_pano.py
old mode 100644
new mode 100755
diff --git a/lantern_scripts/render_all.py b/lantern_scripts/render_all.py
old mode 100644
new mode 100755
diff --git a/lantern_scripts/res_table_1.py b/lantern_scripts/res_table_1.py
old mode 100644
new mode 100755
diff --git a/lantern_scripts/un_gamma.py b/lantern_scripts/un_gamma.py
old mode 100644
new mode 100755
index 9253534ef..bc2310475
--- a/lantern_scripts/un_gamma.py
+++ b/lantern_scripts/un_gamma.py
@@ -17,6 +17,9 @@ if __name__ == "__main__":
     parser.add_argument('--out_dir', type=str, default='/Users/momo/Desktop/Pandora-HDR_NeRF/final_results/Henry_Gagnon_red_and_blue/PanoHDR-NeRF/pano/')
     args = parser.parse_args()
 
+    if not os.path.exists(args.out_dir):
+        os.makedirs(args.out_dir)
+
     test_images = []
     for path in Path(args.data_dir).rglob('*.exr'):
         test_images.append(path)
diff --git a/lantern_scripts/un_mu_law.py b/lantern_scripts/un_mu_law.py
index d907f75ca..1e3e28dfe 100755
--- a/lantern_scripts/un_mu_law.py
+++ b/lantern_scripts/un_mu_law.py
@@ -15,14 +15,19 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('--data_dir', type=str, default='/Users/momo/Desktop/Pandora-HDR_NeRF/final_results/meeting_room/linearization_then_nerf/fast_compressed_mu_law/')
     parser.add_argument('--out_dir', type=str, default='//Users/momo/Desktop/Pandora-HDR_NeRF/final_results/meeting_room/linearization_then_nerf/fast_expo/')
+    parser.add_argument('--is_jpg', action='store_true')
     args = parser.parse_args()
 
     if not os.path.exists(args.out_dir):
         os.makedirs(args.out_dir)
 
     test_images = []
-    for path in Path(args.data_dir).rglob('*.exr'):
-        test_images.append(path)
+    if args.is_jpg:
+        for path in Path(args.data_dir).rglob('*.jpg'):
+            test_images.append(path)
+    else:
+        for path in Path(args.data_dir).rglob('*.exr'):
+            test_images.append(path)
     
     for pano_addr in tqdm(test_images):
         pano_addr = str(pano_addr)
@@ -39,4 +44,7 @@ if __name__ == "__main__":
 
         data = np.float32(data)
         data = cv2.cvtColor(data, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(out_dir, data)
\ No newline at end of file
+        if args.is_jpg:
+            cv2.imwrite(out_dir, data*255)
+        else:
+            cv2.imwrite(out_dir, data)
\ No newline at end of file
diff --git a/linearization_then_nerf_evaluation.sh b/linearization_then_nerf_evaluation.sh
new file mode 100755
index 000000000..96d71c3c4
--- /dev/null
+++ b/linearization_then_nerf_evaluation.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+root_dir=/mnt/data/nerfstudio_ds/real_data/Henry_Gagnon_blue_et_rouge
+dataset_dir=$root_dir/Henry_Gagnon_blue_et_rouge_ns/
+
+opensfm_dir=$root_dir/Henry_Gagnon_blue_et_rouge_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+checkpoint_dir=outputs/Henry_Gagnon_blue_et_rouge_ns/lantern-nerfacto/2024-05-16_030223/
+output_dir=renders/Henry_Gagnon_blue_et_rouge_ns-nerfacto/nerf_then_linearization/2024-05-16_030223_output
+
+#echo "Creating the test set"
+#ns-process-data lantern-GT-HDR --data $dataset_dir --output-dir $dataset_dir --metadata $opensfm_file --checkpoint $checkpoint_dir
+
+#echo "Rendering for the well exposed"
+#ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/well-expo/  --output-format images --image-format exr
+
+#echo "Rendering for the fast exposure"
+#ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/fast-mu-law_compressed/  --output-format images --image-format exr --rendered_output_names rgb_fast
+
+echo "un-mu-law the fast exposure"
+#conda init bash
+conda activate everlight_new
+python lantern_scripts/un_mu_law.py --data_dir $output_dir/fast-mu-law_compressed/ --out_dir $output_dir/fast-expo/ --is_jpg
+
+echo "Combine two exposures"
+python lantern_scripts/combine.py --well_dir $output_dir/well-expo/ --fast_dir $output_dir/fast-expo/ --out_dir $output_dir/pano/ --experiment_location $root_dir
+
+conda activate nerfstudio-HDR
+
+blender --background lantern_scripts/scene_new.blend -P lantern_scripts/hdr_blender.py -- $output_dir/pano/ $output_dir/render/
\ No newline at end of file
diff --git a/nerf_ablation_train_and_test_lab_downstairs.sh b/nerf_ablation_train_and_test_lab_downstairs.sh
new file mode 100755
index 000000000..17bd00b6e
--- /dev/null
+++ b/nerf_ablation_train_and_test_lab_downstairs.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+dataset_name=meeting_room_ns
+method_name=lantern-nerfacto
+root_dir=/mnt/data/nerfstudio_ds/real_data/meeting_room_openSFM
+dataset_dir=$root_dir/$dataset_name/
+opensfm_dir=$root_dir/meeting_room_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+echo "Step one of training"
+ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+--pipeline.model.lantern_steps 1 --pipeline.datamanager.pixel-sampler.lantern_steps 1 --pipeline.model.apply_mu_law False \
+--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --output-dir outputs --timestamp ablation_step_1
+
+echo "Step two of training"
+ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 --pipeline.model.apply_mu_law False \
+--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --load-dir outputs/$dataset_name/lantern-nerfacto/ablation_step_1/nerfstudio_models \
+--output-dir outputs --timestamp ablation_step_2
+
+# checkpoint_dir=outputs/$dataset_name/lantern-nerfacto/ablation_step_2/
+# output_dir=renders/$dataset_name/$method_name/ablation_linearization_then_nerf
+
+# echo "Creating the test set"
+# ns-process-data lantern-GT-HDR --data $dataset_dir --output-dir $dataset_dir --metadata $opensfm_file --checkpoint $checkpoint_dir
+
+# echo "Rendering for the well exposed"
+# ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/well-expo/  --output-format images
+
+# echo "Rendering for the fast exposure"
+# ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/fast-expo/  --output-format images --rendered_output_names rgb_fast
+
+# #conda init bash
+# conda activate everlight_new
+
+# echo "Combine two exposures"
+# python lantern_scripts/combine.py --well_dir $output_dir/well-expo/ --fast_dir $output_dir/fast-expo/ --out_dir $output_dir/pano/ --experiment_location $root_dir
+
+# conda activate nerfstudio-HDR
+
+# blender --background lantern_scripts/scene_new.blend -P lantern_scripts/hdr_blender.py -- $output_dir/pano/ $output_dir/render/
\ No newline at end of file
diff --git a/nerf_then_linearization_train_and_test.sh b/nerf_then_linearization_train_and_test.sh
new file mode 100755
index 000000000..b68972b50
--- /dev/null
+++ b/nerf_then_linearization_train_and_test.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+dataset_name=meeting_room_ns
+method_name=lantern-nerfacto
+root_dir=/mnt/data/nerfstudio_ds/real_data/meeting_room_openSFM
+dataset_dir=$root_dir/$dataset_name/
+opensfm_dir=$root_dir/meeting_room_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+echo "Step one of training"
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 1 --pipeline.datamanager.pixel-sampler.lantern_steps 1 --pipeline.model.apply_mu_law False \
+#--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --output-dir outputs --timestamp step_1
+
+echo "Step two of training"
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 --pipeline.model.apply_mu_law False \
+#--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+#--output-dir outputs --timestamp step_2
+
+checkpoint_dir=outputs/$dataset_name/lantern-nerfacto/step_2/
+output_dir=renders/$dataset_name/$method_name/ours_results
+
+echo "Creating the test set"
+ns-process-data lantern-GT-HDR --data $dataset_dir --output-dir $dataset_dir --metadata $opensfm_file --checkpoint $checkpoint_dir
+
+echo "Rendering for the well exposed"
+ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/well-expo/  --output-format images
+
+echo "Rendering for the fast exposure"
+ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/fast-expo/  --output-format images --rendered_output_names rgb_fast
+
+#conda init bash
+conda activate everlight_new
+
+echo "Combine two exposures"
+python lantern_scripts/combine.py --well_dir $output_dir/well-expo/ --fast_dir $output_dir/fast-expo/ --out_dir $output_dir/pano/ --do_linearization --experiment_location $root_dir
+
+conda activate nerfstudio-HDR
+
+blender --background lantern_scripts/scene_new.blend -P lantern_scripts/hdr_blender.py -- $output_dir/pano/ $output_dir/render/
\ No newline at end of file
diff --git a/nerf_training_lab_downstairs.sh b/nerf_training_lab_downstairs.sh
new file mode 100755
index 000000000..833de59f9
--- /dev/null
+++ b/nerf_training_lab_downstairs.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+dataset_name=lab_downstairs_ns
+method_name=lantern-nerfacto
+root_dir=/mnt/data/nerfstudio_ds/real_data/lab_downstairs_data
+dataset_dir=$root_dir/$dataset_name/
+opensfm_dir=$root_dir/lab_downstairs_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+echo "Step one of training"
+ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+--pipeline.model.lantern_steps 1 --pipeline.datamanager.pixel-sampler.lantern_steps 1 \
+--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --output-dir outputs --timestamp step_1
+
+echo "Step two of training"
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 --pipeline.model.apply_mu_law False \
+#--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+#--output-dir outputs --timestamp step_2
+
+ ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+ --pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 \
+ --pipeline.datamanager.camera-optimizer.mode off --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+ --output-dir outputs --timestamp step_2
\ No newline at end of file
diff --git a/nerf_training_lvsn.sh b/nerf_training_lvsn.sh
new file mode 100755
index 000000000..5e4f3c1e1
--- /dev/null
+++ b/nerf_training_lvsn.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+dataset_name=LVSN_LAB_OpenSFM
+method_name=lantern-nerfacto
+root_dir=/mnt/data/nerfstudio_ds/real_data/lab_ground_floor_wb_3500/openSFM_full_lvsn
+dataset_dir=$root_dir/$dataset_name/
+
+echo "Step one of training"
+# ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+# --pipeline.model.lantern_steps 1 --pipeline.datamanager.pixel-sampler.lantern_steps 1 --pipeline.model.apply_mu_law False \
+# --pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --output-dir outputs --timestamp step_1
+
+echo "Step two of training"
+ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 --pipeline.model.apply_mu_law False \
+--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+--output-dir outputs --timestamp step_2
+
+#  ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#  --pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 \
+#  --pipeline.datamanager.camera-optimizer.mode off --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+#  --output-dir outputs --timestamp step_2
\ No newline at end of file
diff --git a/nerf_training_my_apartment.sh b/nerf_training_my_apartment.sh
new file mode 100755
index 000000000..de5854d53
--- /dev/null
+++ b/nerf_training_my_apartment.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+dataset_name=my_apartment_ns
+method_name=lantern-nerfacto
+root_dir=/mnt/data/nerfstudio_ds/real_data/my_apartment_corrected
+dataset_dir=$root_dir/$dataset_name/
+opensfm_dir=$root_dir/my_apartment_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+echo "Step one of training"
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 1 --pipeline.datamanager.pixel-sampler.lantern_steps 1 --pipeline.model.apply_mu_law False \
+#--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --output-dir outputs --timestamp step_1
+
+echo "Step two of training"
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 --pipeline.model.apply_mu_law False \
+#--pipeline.datamanager.camera-optimizer.mode off --viewer.quit-on-train-completion True --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+#--output-dir outputs --timestamp step_2
+
+#ns-train lantern-nerfacto --data $dataset_dir --viewer.websocket-port 8008 --pipeline.datamanager.train-num-images-to-sample-from 1800 \
+#--pipeline.model.lantern_steps 2 --pipeline.datamanager.pixel-sampler.lantern_steps 2 \
+#--pipeline.datamanager.camera-optimizer.mode off --load-dir outputs/$dataset_name/lantern-nerfacto/step_1/nerfstudio_models \
+#--output-dir outputs --timestamp step_2
\ No newline at end of file
diff --git a/nerfstudio/data/pixel_samplers.py b/nerfstudio/data/pixel_samplers.py
index 2e1d3fed4..67c6c6b9b 100644
--- a/nerfstudio/data/pixel_samplers.py
+++ b/nerfstudio/data/pixel_samplers.py
@@ -392,24 +392,24 @@ class PixelSampler:
                         self.fast_image_batch, int(0.0 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True, only_light_sources=True
                     )
                 else:
-                    # well_pixel_batch = self.collate_image_dataset_batch(
-                    #     self.well_image_batch, int(0.2 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image
-                    # )
-                    # fast_pixel_batch = self.collate_image_dataset_batch(
-                    #     self.fast_image_batch, int(0.3 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True
-                    # )
-                    # light_pixel_batch = self.collate_image_dataset_batch(
-                    #     self.fast_image_batch, int(0.5 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True, only_light_sources=True
-                    # )
                     well_pixel_batch = self.collate_image_dataset_batch(
-                        self.well_image_batch, int(0.0 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image
+                        self.well_image_batch, int(0.2 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image
                     )
                     fast_pixel_batch = self.collate_image_dataset_batch(
                         self.fast_image_batch, int(0.3 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True
                     )
                     light_pixel_batch = self.collate_image_dataset_batch(
-                        self.fast_image_batch, int(0.7 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True, only_light_sources=True
+                        self.fast_image_batch, int(0.5 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True, only_light_sources=True
                     )
+                    # well_pixel_batch = self.collate_image_dataset_batch(
+                    #     self.well_image_batch, int(0.0 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image
+                    # )
+                    # fast_pixel_batch = self.collate_image_dataset_batch(
+                    #     self.fast_image_batch, int(0.3 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True
+                    # )
+                    # light_pixel_batch = self.collate_image_dataset_batch(
+                    #     self.fast_image_batch, int(0.7 * self.num_rays_per_batch), keep_full_image=self.config.keep_full_image, is_fast_expo=True, only_light_sources=True
+                    # )
                 
                 pixel_batch = well_pixel_batch #.copy()
                 for key, value in fast_pixel_batch.items():
diff --git a/nerfstudio/lantern/config.py b/nerfstudio/lantern/config.py
index 0aacdecdd..e09036291 100644
--- a/nerfstudio/lantern/config.py
+++ b/nerfstudio/lantern/config.py
@@ -30,12 +30,12 @@ def Get_lantern_config():
             dataparser=NerfstudioDataParserConfig(),
             train_num_rays_per_batch=8192,
             eval_num_rays_per_batch=8192,
-            camera_optimizer=CameraOptimizerConfig(
-                mode="SO3xR3",
-                optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
-                scheduler=ExponentialDecaySchedulerConfig(lr_final=6e-6, max_steps=200000),
-            ),
-            # camera_optimizer=None,
+            # camera_optimizer=CameraOptimizerConfig(
+            #     mode="SO3xR3",
+            #     optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2),
+            #     scheduler=ExponentialDecaySchedulerConfig(lr_final=6e-6, max_steps=200000),
+            # ),
+            camera_optimizer=None,
         ),
         model=LanternModelConfig(eval_num_rays_per_chunk=1 << 15),
     ),
diff --git a/nerfstudio/lantern/field.py b/nerfstudio/lantern/field.py
index aee80d9dd..d9cb8d9ea 100644
--- a/nerfstudio/lantern/field.py
+++ b/nerfstudio/lantern/field.py
@@ -130,11 +130,11 @@ class LanternNerfactoField(NerfactoField):
     
     def get_density(self, ray_samples: RaySamples) -> Tuple[Tensor, Tensor]:
         """Computes and returns the densities."""
-        if self.second_step:
-            with torch.no_grad():
-                density, base_mlp_out = super().get_density(ray_samples)
-        else:
-            density, base_mlp_out = super().get_density(ray_samples)
+        # if self.second_step:
+        #     with torch.no_grad():
+        #         density, base_mlp_out = super().get_density(ray_samples)
+        # else:
+        density, base_mlp_out = super().get_density(ray_samples)
         return density, base_mlp_out
     
     
diff --git a/nerfstudio/utils/colormaps.py b/nerfstudio/utils/colormaps.py
index 81ddad3ee..07d6b1415 100644
--- a/nerfstudio/utils/colormaps.py
+++ b/nerfstudio/utils/colormaps.py
@@ -66,11 +66,11 @@ def apply_colormap(
     # default for rgb images
     if image.shape[-1] == 3:
         # if(colormap_options.exposure_scale != 0 or image.max() > 1):
-        # if colormap_options.colormap == 'inverse-mu':
-        #     u = 5000.
-        #     img_uncompress = torch.exp(image * torch.log(torch.tensor(u+1.))) - 1.
-        #     img_uncompress /= u
-        #     image = img_uncompress
+        if colormap_options.colormap == 'inverse-mu':
+            u = 5000.
+            img_uncompress = torch.exp(image * torch.log(torch.tensor(u+1.))) - 1.
+            img_uncompress /= u
+            image = img_uncompress
         img_gamma_22 = image
         img_gamma_22 *= 2.**(colormap_options.exposure_scale)
         # img_gamma_22 = torch.pow(img_gamma_22, 1./2.2)
diff --git a/pano_hdr_nerf_evaluation.sh b/pano_hdr_nerf_evaluation.sh
new file mode 100755
index 000000000..53f227471
--- /dev/null
+++ b/pano_hdr_nerf_evaluation.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+echo "Evaluation for linearization then nerf pipeline"
+
+opensfm_dir=/mnt/data/nerfstudio_ds/real_data/my_apartment_corrected/my_apartment_openSFM
+opensfm_file=$opensfm_dir/reconstruction.json
+
+dataset_dir=/mnt/data/nerfstudio_ds/real_data/my_apartment_corrected/pano_hdr_nerf/pano_hdr_nerf_my_apt/
+
+checkpoint_dir=outputs/pano_hdr_nerf_my_apt/panoHDR-nerfacto/2024-05-18_160257/
+output_dir=/mnt/workspace/lantern/nerfstudio-HDR/renders/pano_hdr_nerf_my_apt
+
+
+echo "Creating the test set"
+ns-process-data lantern-GT-HDR --data $dataset_dir --output-dir $dataset_dir --metadata $opensfm_file --checkpoint $checkpoint_dir
+
+echo "Rendering for the well exposed"
+ns-render camera-path --load-config $checkpoint_dir/config.yml --camera-path-filename $dataset_dir/GT_transforms.json --output-path $output_dir/pano_gamma_compressed/  --output-format images --image-format exr
+
+conda activate everlight_new
+
+python lantern_scripts/un_gamma.py --data_dir $output_dir/pano_gamma_compressed/ --out_dir $output_dir/pano/
+
+conda activate nerfstudio-HDR
+
+blender --background lantern_scripts/scene_new_my_apt.blend -P lantern_scripts/hdr_blender.py -- $output_dir/pano/ $output_dir/render/
\ No newline at end of file