last commit before SIG ASIA

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)

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()

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'

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))

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()

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)

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)
+        if args.is_jpg:
+            cv2.imwrite(out_dir, data*255)
+        else:
+            cv2.imwrite(out_dir, data)