-Fixed PR

-TODO: find alignement factor

configs/train_tapecam_measured_background.yaml

@@ -0,0 +1,19 @@
+# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape
+defaults:
+  - train_mirflickr_tape
+  - _self_
+
+wandb_project:
+device_ids:
+
+
+# Dataset
+files:
+  dataset: Lensless/TapeCam-Mirflickr-Ambient
+  background_snr_range: [0,0]
+  image_res: [507, 380]
+
+alignment:
+  # when there is no downsampling
+  top_left: [45, 95]  # height, width
+  height: 250

configs/train_tapecam_simulated_background.yaml

@@ -9,6 +9,5 @@ device_ids:
 
 # Dataset
 files:
-  dataset: Lensless/TapeCam-Mirflickr-Ambient
-  background_fp:
+  background_fp: ""
   background_snr_range: [0,0]

lensless/utils/dataset.py

@@ -1276,7 +1276,6 @@ def __init__(
         flip_lensed=False,
         downsample=1,
         downsample_lensed=1,
-        downsample_background=1,
         display_res=None,
         sensor="rpi_hq",
         slm="adafruit",
@@ -1310,8 +1309,6 @@ def __init__(
             If True, lensless images and PSF are rotated 180 degrees. Lensed/original image is not rotated! By default False.
         downsample : float, optional
             Downsample factor of the lensless images, by default 1.
-        downsample : float, optional
-            Downsample factor of the background images, by default 1.
         downsample_lensed : float, optional
             Downsample factor of the lensed images, by default 1.
         display_res : tuple, optional
@@ -1362,7 +1359,6 @@ def __init__(
         data_0 = self.dataset[0]
         self.downsample_lensless = downsample
         self.downsample_lensed = downsample_lensed
-        self.downsample_background = downsample_background
         lensless = np.array(data_0["lensless"])
         if "ambient" in data_0.keys():
             self.measured_bg = True
@@ -1593,7 +1589,7 @@ def _get_images_pair(self, idx):
             background_np = (
                 resize(
                     background_np,
-                    factor=1 / self.downsample_background,
+                    factor=1 / self.downsample,
                     interpolation=cv2.INTER_NEAREST,
                 )
                 if not None
@@ -1638,32 +1634,18 @@ def _get_images_pair(self, idx):
             lensed = resize(
                 lensed_np, shape=(*self.display_res, 3), interpolation=cv2.INTER_NEAREST
             )
-            background = (
-                resize(background_np, shape=(*self.display_res, 3), interpolation=cv2.INTER_NEAREST)
-                if not None
-                else None
-            )
         elif self.downsample_lensed != 1.0:
             lensed = resize(
                 lensed_np,
                 factor=1 / self.downsample_lensed,
                 interpolation=cv2.INTER_NEAREST,
             )
-            background = (
-                resize(
-                    background_np,
-                    factor=1 / self.downsample_lensed,
-                    interpolation=cv2.INTER_NEAREST,
-                )
-                if not None
-                else None
-            )
 
         return lensless, lensed, background if background is not None else None
 
     def __getitem__(self, idx):
         lensless, lensed, background = self._get_images_pair(idx)
-        if not self.simulate_lensless:  # TODO apply transformation to bg as well?
+        if not self.simulate_lensless:
             if self.rotate:
                 lensless = torch.rot90(lensless, dims=(-3, -2), k=2)
             if self.flipud:
@@ -1732,7 +1714,6 @@ def __getitem__(self, idx):
         # If measured background available in the dataset return it
         elif self.measured_bg:
             return_items.append(background)
-        # TODO push data to gpu in the training loop and hvae a flag that for the simple subtraction does that without pushing the bg to the gpu
         return return_items
 
     def extract_roi(

scripts/recon/train_learning_based.py

@@ -395,8 +395,8 @@ def train_learned(config):
                     rotate_angle,
                     shift,
                 )
-                # save_image(lensed[0].cpu().numpy(), f"lensed_{_idx}.png")
-                save_image(lensed, f"lensed_{_idx}.png")
+                save_image(lensed[0].cpu().numpy(), f"lensed_{_idx}.png")
+                # save_image(lensed, f"lensed_{_idx}.png")
                 if test_set.bg_sim is not None:
                     # Reconstruct and plot background subtracted image
                     reconstruct_save(
@@ -660,14 +660,14 @@ def reconstruct_save(
 ):
     recon = ADMM(psf_recon)
 
-    #    recon.set_data(lensless.to(psf_recon.device))
-    recon.set_data(torch.from_numpy(lensless).to(psf_recon.device))
+    recon.set_data(lensless.to(psf_recon.device))
+    # recon.set_data(torch.from_numpy(lensless).to(psf_recon.device))
     res = recon.apply(disp_iter=None, plot=False, n_iter=10)
     res_np = res[0].cpu().numpy()
     res_np = res_np / res_np.max()
     lensed_np = lensed[0]  # .cpu().numpy()
 
-    lensless_np = lensless  # [0]#.cpu().numpy()
+    lensless_np = lensless.cpu().numpy()  # [0]#.cpu().numpy()
     save_image(lensless_np, f"lensless_raw_{_idx}.png")
 
     # -- plot lensed and res on top of each other