Merge branch 'main' into expose_sim_param

README.rst

@@ -168,8 +168,7 @@ directory):
    pip install -r rpi_requirements.txt
 
    # test on-device camera capture (after setting up the camera)
-   source lensless_env/bin/activate
-   python scripts/measure/on_device_capture.py
+   (lensless_env) python scripts/measure/on_device_capture.py
 
 You may still need to manually install ``numpy`` and/or ``scipy`` with ``pip`` in case libraries (e.g. ``libopenblas.so.0``) cannot be detected.
 

configs/analyze_dataset.yaml

@@ -1,9 +1,11 @@
+# python scripts/measure/analyze_measured_dataset.py
 hydra:
   job:
     chdir: True    # change to output folder
 
 dataset_path: null
-desired_range: [150, 254]
+desired_range: [150, 255]
+saturation_percent: 0.05
 delete_bad: False
 n_files: null
 start_idx: null

configs/collect_dataset.yaml

@@ -24,6 +24,7 @@ min_level: 200
 max_tries: 6
 
 masks: null   # for multi-mask measurements
+recon: null   # parameters for reconstruction (for debugging purposes, not recommended to do during actual measurement as it will significantly increase the time)
 
 # -- display parameters
 display:
@@ -41,10 +42,12 @@ display:
 
 capture:
   skip: False    # to test looping over displaying images
-  config_pause: 2
+  config_pause: 3
   iso: 100
   res: null
   down: 4
   exposure: 0.02    # min exposure
   awb_gains: [1.9, 1.2]  # red, blue
-  # awb_gains: null
+  # awb_gains: null
+  fact_increase: 2   # multiplicative factor to increase exposure
+  fact_decrease: 1.5

configs/collect_mirflickr_fza.yaml

@@ -0,0 +1,32 @@
+# python scripts/measure/collect_dataset_on_device.py -cn collect_mirflickr_fza
+defaults:
+  - collect_dataset
+  - _self_
+
+input_dir: /mnt/mirflickr/all
+min_level: 170
+
+# FOR TESTING PURPOSE
+output_dir: data/fza_test   # RPi won't have enough memory for full measured dataset
+max_tries: 0
+recon:
+  psf: data/psf/tape_rgb.png  # TODO: set correct PSF
+  n_iter: 10
+# # FOR FINAL MEASUREMENT
+# max_tries: 2
+# output_dir: /mnt/mirflickr/fza_10K
+
+# files to measure
+n_files: 25000
+
+# -- display parameters
+display:
+  image_res: [900, 1200]
+  vshift: -26
+  brightness: 90
+  delay: 1
+
+capture:
+  down: 8
+  exposure: 0.7
+  awb_gains: [1.6, 1.2]  # red, blue, TODO for your mask

lensless/recon/recon.py

@@ -494,9 +494,9 @@ def _get_numpy_data(self, data):
     def apply(
         self,
         n_iter=None,
-        disp_iter=10,
+        disp_iter=-1,
         plot_pause=0.2,
-        plot=True,
+        plot=False,
         save=False,
         gamma=None,
         ax=None,

lensless/utils/image.py

@@ -221,6 +221,7 @@ def get_max_val(img, nbits=None):
 def bayer2rgb_cc(
     img,
     nbits,
+    down=None,
     blue_gain=None,
     red_gain=None,
     black_level=RPI_HQ_CAMERA_BLACK_LEVEL,
@@ -269,6 +270,10 @@ def bayer2rgb_cc(
     # demosaic Bayer data
     img = cv2.cvtColor(img, cv2.COLOR_BayerRG2RGB)
 
+    # downsample
+    if down is not None:
+        img = resize(img[None, ...], factor=1 / down, interpolation=cv2.INTER_CUBIC)[0]
+
     # correction
     img = img - black_level
     if red_gain:
@@ -277,6 +282,7 @@ def bayer2rgb_cc(
         img[:, :, 2] *= blue_gain
     img = img / (2**nbits - 1 - black_level)
     img[img > 1] = 1
+
     img = (img.reshape(-1, 3, order="F") @ ccm.T).reshape(img.shape, order="F")
     img[img < 0] = 0
     img[img > 1] = 1

lensless/utils/io.py

@@ -588,31 +588,39 @@ def save_image(img, fp, max_val=255, normalize=True):
 
     img_tmp = img.copy()
 
-    if img_tmp.dtype == np.uint16 or img_tmp.dtype == np.uint8:
-        img_tmp = img_tmp.astype(np.float32)
-
     if normalize:
+
+        if img_tmp.dtype == np.uint16 or img_tmp.dtype == np.uint8:
+            img_tmp = img_tmp.astype(np.float32)
+
         img_tmp -= img_tmp.min()
         img_tmp /= img_tmp.max()
-    else:
-        normalized = False
-        if img_tmp.min() < 0:
-            img_tmp -= img_tmp.min()
-            normalize = True
-        if img_tmp.max() > 1:
-            img_tmp /= img_tmp.max()
-            normalize = True
-        if normalized:
-            print(f"Warning (out of range): {fp} normalizing data to [0, 1]")
-
-    if img_tmp.dtype == np.float64 or img_tmp.dtype == np.float32:
         img_tmp *= max_val
         img_tmp = img_tmp.astype(np.uint8)
 
-    # RGB
+    else:
+
+        if img_tmp.dtype == np.float64 or img_tmp.dtype == np.float32:
+            # check within [0, 1] and convert to uint8
+
+            normalized = False
+            if img_tmp.min() < 0:
+                img_tmp -= img_tmp.min()
+                normalized = True
+            if img_tmp.max() > 1:
+                img_tmp /= img_tmp.max()
+                normalized = True
+            if normalized:
+                print(f"Warning (out of range): {fp} normalizing data to [0, 1]")
+            img_tmp *= max_val
+            img_tmp = img_tmp.astype(np.uint8)
+
+    # save
     if len(img_tmp.shape) == 3 and img_tmp.shape[2] == 3:
+        # RGB
         img_tmp = Image.fromarray(img_tmp)
     else:
+        # grayscale
         img_tmp = Image.fromarray(img_tmp.squeeze())
     img_tmp.save(fp)
 

rpi_requirements.txt

@@ -5,4 +5,5 @@ matplotlib>=3.4.2
 hydra-code
 paramiko
 numpy>=1.24.2
-scipy>=1.6.0
+scipy>=1.6.0
+git+https://github.com/ebezzam/slm-controller.git

scripts/measure/analyze_measured_dataset.py

@@ -15,6 +15,19 @@
 import matplotlib.pyplot as plt
 import time
 import tqdm
+import re
+
+
+def convert(text):
+    return int(text) if text.isdigit() else text.lower()
+
+
+def alphanum_key(key):
+    return [convert(c) for c in re.split("([0-9]+)", key)]
+
+
+def natural_sort(arr):
+    return sorted(arr, key=alphanum_key)
 
 
 @hydra.main(version_base=None, config_path="../../configs", config_name="analyze_dataset")
@@ -24,13 +37,14 @@ def analyze_dataset(config):
     desired_range = config.desired_range
     delete_bad = config.delete_bad
     start_idx = config.start_idx
+    saturation_percent = config.saturation_percent
 
     assert (
         folder is not None
     ), "Must specify folder to analyze in config or through command line (folder=PATH)."
 
     # get all PNG files in folder
-    files = sorted(glob.glob(os.path.join(folder, "*.png")))
+    files = natural_sort(glob.glob(os.path.join(folder, "*.png")))
     print("Found {} files".format(len(files)))
     if start_idx is not None:
         files = files[start_idx:]
@@ -48,10 +62,9 @@ def analyze_dataset(config):
         im = np.array(Image.open(fn))
         max_val = im.max()
         max_vals.append(max_val)
+        saturation_ratio = np.sum(im >= desired_range[1]) / im.size
 
-        # if out of desired range, print filename
-        if max_val < desired_range[0] or max_val > desired_range[1]:
-            # print("File {} has max value {}".format(fn, max_val))
+        if max_val < desired_range[0]:
             n_bad_files += 1
             bad_files.append(fn)
 
@@ -61,6 +74,28 @@ def analyze_dataset(config):
             else:
                 print("File {} has max value {}".format(fn, max_val))
 
+        elif saturation_ratio > saturation_percent:
+            n_bad_files += 1
+            bad_files.append(fn)
+
+            if delete_bad:
+                os.remove(fn)
+                print("REMOVED file {}".format(fn))
+            else:
+                print("File {} has saturation ratio {}".format(fn, saturation_ratio))
+
+        # # if out of desired range, print filename
+        # if max_val < desired_range[0] or saturation_ratio > saturation_percent:
+        #     # print("File {} has max value {}".format(fn, max_val))
+        #     n_bad_files += 1
+        #     bad_files.append(fn)
+
+        #     if delete_bad:
+        #         os.remove(fn)
+        #         print("REMOVED file {}".format(fn))
+        #     else:
+        #         print("File {} has max value {}".format(fn, max_val))
+
     proc_time = time.time() - start_time
     print("Went through {} files in {:.2f} seconds".format(len(files), proc_time))
     print(
@@ -69,6 +104,14 @@ def analyze_dataset(config):
         )
     )
 
+    # plot histogram
+    output_folder = os.getcwd()
+    output_fp = os.path.join(output_folder, "max_vals.png")
+    plt.hist(max_vals, bins=100)
+    plt.savefig(output_fp)
+
+    print("Saved histogram to {}".format(output_fp))
+
     # command line input on whether to delete bad files
     if not delete_bad:
         response = None
@@ -80,14 +123,6 @@ def analyze_dataset(config):
         else:
             print("Not deleting bad files")
 
-    # plot histogram
-    output_folder = os.getcwd()
-    output_fp = os.path.join(output_folder, "max_vals.png")
-    plt.hist(max_vals, bins=100)
-    plt.savefig(output_fp)
-
-    print("Saved histogram to {}".format(output_fp))
-
 
 if __name__ == "__main__":
     analyze_dataset()