Merge branch 'main' into measured_ambient

CHANGELOG.rst

@@ -37,6 +37,7 @@ Changed
 Bugfix
 ~~~~~~
 
+- Wavelength input to ``compute_psf`` for Mask class.
 - Computation of average metric in batches.
 - Support for grayscale PSF for RealFFTConvolve2D.
 - Calling model.eval() before inference, and model.train() before training.

configs/analyze_dataset.yaml

@@ -4,7 +4,7 @@ hydra:
     chdir: True    # change to output folder
 
 dataset_path: null
-desired_range: [150, 255]
+desired_range: [10, 255]
 saturation_percent: 0.05
 delete_bad: False
 n_files: null

configs/upload_multilens_mirflickr_ambient.yaml

@@ -0,0 +1,20 @@
+# python scripts/data/upload_dataset_huggingface.py -cn upload_multilens_mirflickr_ambient
+defaults:
+  - upload_dataset_huggingface
+  - _self_
+
+repo_id: "Lensless/MultiLens-Mirflickr-Ambient"
+n_files:
+test_size: 0.15
+lensless:
+  dir: /dev/shm/all_measured_20240813-183259
+  ambient: True
+  ext: ".png"
+
+lensed:
+  dir: data/mirflickr/mirflickr
+  ext: ".jpg"
+
+files:
+  psf: data/multilens_psf.png
+  measurement_config: data/collect_dataset_background_multilens.yaml

docs/source/examples.rst

@@ -16,15 +16,16 @@ where the programmable is a low-cost LCD:
 - `DigiCam: Single-Shot Lensless Sensing with a Low-Cost Programmable Mask <https://colab.research.google.com/drive/1t59uyZMMyCUYVHGXdqdlNlDlb--FL_3P#scrollTo=t9o50zTf3oUg>`__
 - `Towards Scalable and Secure Lensless Imaging with a Programmable Mask <https://colab.research.google.com/drive/1YGfs9p4T4NefX8GemVWwtrw4aX8zH1qu#scrollTo=tipedTe4vGwD>`__
 
-Reconstruction method
+Reconstruction
 ---------------------
 
 Learning-based reconstruction methods:
 
 - `A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction <https://colab.research.google.com/drive/1Wgt6ZMRZVuctLHaXxk7PEyPaBaUPvU33>`__
-- `Aligning images for training <https://colab.research.google.com/drive/1c6kUbiB5JO1vro0-IMd-YDDP1g7NFXv3#scrollTo=MtN7GWCIrBKr>`__
+- `Aligning images for training & background subtraction demo <https://drive.google.com/file/d/1oWy07xT_5-_Xki6g9TbOiBcxDv0bN-96/view?usp=drive_link>`__
+- `Aligning images for training (DigiCam) <https://colab.research.google.com/drive/1c6kUbiB5JO1vro0-IMd-YDDP1g7NFXv3#scrollTo=MtN7GWCIrBKr>`__
 
-Mask Design and fabrication
+Mask Design and Fabrication
 ---------------------------
 
 - `Multi-lens array design <https://drive.google.com/file/d/1IIGjdPUD5qqq4kWjDp50OWnIvHPVdvmp/view?usp=sharing>`__

lensless/hardware/mask.py

@@ -219,7 +219,7 @@ def compute_psf(self, distance_sensor=None, wavelength=None, intensity=True):
 
         if self.use_torch:
             psf = torch.zeros(
-                tuple(self.resolution) + (len(self.psf_wavelength),),
+                tuple(self.resolution) + (len(wavelength),),
                 dtype=torch.complex64,
                 device=self.torch_device,
             )

scripts/measure/analyze_measured_dataset.py

@@ -2,7 +2,7 @@
 Check maximum pixel value of images and check for saturation / underexposure.
 
 ```
-python scripts/measure/analyze_measured_dataset.py folder=PATH
+python scripts/measure/analyze_measured_dataset.py dataset_path=PATH
 ```
 """
 
@@ -123,6 +123,31 @@ def analyze_dataset(config):
         else:
             print("Not deleting bad files")
 
+    # check for matching background file
+    files_bg = natural_sort(glob.glob(os.path.join(folder, "black_background*.png")))
+    # -- remove files_bg from files
+    files = [fn for fn in files if fn not in files_bg]
+
+    if len(files_bg) > 0:
+        print("Found {} background files".format(len(files_bg)))
+        # detect files that don't have background
+        files_no_bg = []
+        for fn in files:
+            bn = os.path.basename(fn).split(".")[0]
+            _bg_file = os.path.join(folder, "black_background{}.png".format(bn))
+            if _bg_file not in files_bg:
+                files_no_bg.append(fn)
+
+        print("Found {} files without background".format(len(files_no_bg)))
+        # ask to delete files without background
+        response = None
+        while response not in ["yes", "no"]:
+            response = input("Delete files without background: [yes|no] : ")
+        if response == "yes":
+            for _fn in files_no_bg:
+                if os.path.exists(_fn):  # maybe already deleted before
+                    os.remove(_fn)
+
 
 if __name__ == "__main__":
     analyze_dataset()