Commit version v0.15.0

HISTORY.md

@@ -1,12 +1,20 @@
 History
 =======
 
+0.15.0
+---------------------
+
+* Add support to save ground truth image data to the Annotations directory. Set sim option `save_ground_truth` to `true`.
+* Add support for running on CPU with no GPU acceleration.
+* Add CZML options for sensor visualization and object billboard image.
+
+
 0.14.0
 ---------------------
 
-Add vector math library.
-Add CZML output for sensor visualization.
-Fix objects not updating properly when image renderer is off.
+* Add vector math library.
+* Add CZML output for sensor visualization.
+* Fix objects not updating properly when image renderer is off.
 
 
 0.13.1

Makefile

@@ -70,6 +70,12 @@ coverage: ## check code coverage quickly with the default python3
 	coverage html
 	$(BROWSER) htmlcov/index.html
 
+cpucoverage: ## check code coverage quickly with the default python3
+	CUDA_VISIBLE_DEVICES="" && coverage run --source satsim -m pytest
+	coverage report -m
+	coverage html
+	$(BROWSER) htmlcov/index.html
+
 docs: ## generate Sphinx HTML documentation, including API docs
 	rm -f docs/satsim.rst
 	rm -f docs/modules.rst
@@ -105,11 +111,11 @@ install: clean ## install the package to the active python3's site-packages
 	@echo  IMPORTANT: You may need to close and restart your shell after running "make install".
 
 docker: docs dist
-	docker build -t satsim:0.14.0 -t satsim:latest -f docker/ubuntu20.04_cuda11.2_py3.8.dockerfile .
+	docker build -t satsim:0.15.0 -t satsim:latest -f docker/ubuntu20.04_cuda11.2_py3.8.dockerfile .
 
 dind:
 	docker run --rm -it -v $(CURDIR):/workspace/ -w /workspace python:3.8-bullseye ./build.sh
-	docker build -t satsim:0.14.0 -t satsim:latest -f docker/ubuntu20.04_cuda11.2_py3.8.dockerfile .
+	docker build -t satsim:0.15.0 -t satsim:latest -f docker/ubuntu20.04_cuda11.2_py3.8.dockerfile .
 
 uninstall: clean
 	cat .install.log | xargs rm -rf

docker/ubuntu20.04_cuda11.2_py3.8.dockerfile

@@ -25,25 +25,25 @@ ENV LANG='en_US.UTF-8' LANGUAGE='en_US:en' LC_ALL='en_US.UTF-8'
 RUN pip3 install --no-cache-dir --upgrade pip setuptools
 
 # copy and install satsim wheel file
-ENV SATSIM_VERSION='0.14.0'
-COPY dist/satsim-0.14.0-py2.py3-none-any.whl /tmp
+ENV SATSIM_VERSION='0.15.0'
+COPY dist/satsim-0.15.0-py2.py3-none-any.whl /tmp
 
 # install python prereqs and satsim
 RUN pip3 --no-cache-dir install \
         imageio==2.15.0 \
         tensorflow~=2.8.0 \
         tensorflow-addons~=0.16.1 \
-        tmp/satsim-0.14.0-py2.py3-none-any.whl
+        tmp/satsim-0.15.0-py2.py3-none-any.whl
 
 RUN pip3 --no-cache-dir install \
         jupyterlab \
         scikit-learn \
         virtualenv
 
 # copy and install satsim wheel file
-ENV SATSIM_VERSION='0.14.0'
-COPY dist/satsim-0.14.0-py2.py3-none-any.whl /tmp
-RUN pip3 install tmp/satsim-0.14.0-py2.py3-none-any.whl
+ENV SATSIM_VERSION='0.15.0'
+COPY dist/satsim-0.15.0-py2.py3-none-any.whl /tmp
+RUN pip3 install tmp/satsim-0.15.0-py2.py3-none-any.whl
 
 RUN mkdir /workspace
 WORKDIR /workspace

docs/conf.py

@@ -58,9 +58,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = '0.14.0'
+version = '0.15.0'
 # The full version, including alpha/beta/rc tags.
-release = '0.14.0'
+release = '0.15.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

satsim/__init__.py

@@ -4,7 +4,7 @@
 
 __author__ = """Alex Cabello"""
 __email__ = '[email protected]'
-__version__ = '0.14.0'
+__version__ = '0.15.0'
 
 from .satsim import gen_images, gen_multi, image_generator
 from .config import load_json, load_yaml

satsim/dataset/augment.py

@@ -46,7 +46,7 @@ def _augment_satnet_with_satsim(image, bboxs, filename=None, annotational_filena
         sspc['augment']['image']['post'] = tf.squeeze(image)
 
         ig = image_generator(sspc, with_meta=True)
-        fpa_digital, frame_num, astrometrics, obs_os_pix, fpa_conv_star, fpa_conv_targ, bg_tf, dc_tf, rn_tf, num_shot_noise_samples, obs_cache = ig.__next__()
+        fpa_digital, frame_num, astrometrics, obs_os_pix, fpa_conv_star, fpa_conv_targ, bg_tf, dc_tf, rn_tf, num_shot_noise_samples, obs_cache, ground_truth = ig.__next__()
 
         anno = init_annotation('.', 0, h, w, y_fov, x_fov)
         snr = signal_to_noise_ratio(fpa_conv_targ, tf.squeeze(image) * a2d_gain, rn)

satsim/image/fpa.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from satsim.geometry.transform import rotate_and_translate
-from satsim.util import get_semantic_version
+from satsim.util import get_semantic_version, is_tensorflow_running_on_cpu
 from satsim.math import fftconv2p
 
 
@@ -253,19 +253,18 @@ def add_counts(fpa, r, c, cnt, r_offset=0, c_offset=0):
     Returns:
         A `Tensor`, a reference to the modified input `fpa` image
     """
-    r = tf.cast(r, tf.int32)
-    c = tf.cast(c, tf.int32)
-
-    # TODO no bounds checking in TF-GPU if fpa is int32 type
-    # h,w = fpa.get_shape().as_list()
-    # valid = (r >= 0) & (r < h) & (c >= 0) & (c < w)
-    # r = tf.boolean_mask(r, valid)
-    # c = tf.boolean_mask(c, valid)
-    # cnt = tf.boolean_mask(tf.convert_to_tensor(cnt, dtype=fpa.dtype), valid)
-
-    # r_offset = r_offset
-    # c_offset = c_offset
-    rc = tf.stack([r + r_offset, c + c_offset], axis=1)
+    r = tf.cast(r, tf.int32) + tf.cast(r_offset, tf.int32)
+    c = tf.cast(c, tf.int32) + tf.cast(c_offset, tf.int32)
+
+    # fix for no bounds checking if fpa is int32 or if running CPU
+    if is_tensorflow_running_on_cpu() or fpa.dtype == 'int32':
+        h, w = fpa.get_shape().as_list()
+        valid = (r >= 0) & (r < h) & (c >= 0) & (c < w)
+        r = tf.boolean_mask(r, valid)
+        c = tf.boolean_mask(c, valid)
+        cnt = tf.boolean_mask(tf.convert_to_tensor(cnt, dtype=fpa.dtype), valid)
+
+    rc = tf.stack([r, c], axis=1)
     return tf.compat.v1.tensor_scatter_nd_add(fpa, rc, cnt)
 
 

satsim/image/noise.py

@@ -57,8 +57,9 @@ def add_read_noise(fpa, rn, en=0):
 
     Returns:
         A `Tensor`, the 2D tensor with read noise applied.
+        A `Tensor`, the 2D tensor read noise.
     """
     rn = tf.cast(rn, tf.float32)
     en = tf.cast(rn, tf.float32)
     noise = tf.random.normal(tf.shape(fpa)) * tf.math.sqrt(rn * rn + en * en)
-    return fpa + noise
+    return fpa + noise, noise

satsim/io/czml.py

@@ -71,10 +71,14 @@ def save_czml(ssp, obs_cache, astrometrics, filename):
         alt = site['alt'] * u.m
 
         name = site['name'] if 'name' in site else None
-        label_show = False
 
-        if 'czml' in site:
-            label_show = site['czml'].get('path_show', label_show)
+        # czml
+        czml_config = site['czml'] if 'czml' in site else {}
+        label_show = czml_config.get('label_show', False)
+        cone_show = czml_config.get('cone_show', True)
+        cone_color = czml_config.get('cone_color', [255, 255, 0, 64])
+        billboard_show = czml_config.get('billboard_show', True)
+        billboard_image = czml_config.get('billboard_image', PIC_GROUNDSTATION)
 
         sensor = {
             'y_fov': astrometrics[0]['y_fov'],
@@ -84,7 +88,9 @@ def save_czml(ssp, obs_cache, astrometrics, filename):
             'range': [x['range'] for x in astrometrics],
         }
 
-        extractor.add_ground_station([latitude, longitude, alt], sensor, label_text=name, label_show=label_show)
+        extractor.add_ground_station([latitude, longitude, alt], sensor, label_text=name, label_show=label_show,
+                                     cone_show=cone_show, cone_color=cone_color, billboard_show=billboard_show,
+                                     billboard_image=billboard_image)
 
     # extract objects data
     for i, o in enumerate(ssp['geometry']['obs']['list']):
@@ -102,27 +108,24 @@ def save_czml(ssp, obs_cache, astrometrics, filename):
 
             name = o['name'] if 'name' in o else None
 
-            path_show = (ts_start_ob == t0)
-            path_color = [255, 255, 0]
-            billboard_show = True
             start_interval = time.to_astropy(t0)
             end_interval = time.to_astropy(t2)
-            label_show = False
-
-            if 'czml' in o:
-                label_show = o['czml'].get('label_show', label_show)
-                path_show = o['czml'].get('path_show', path_show)
-                path_color = o['czml'].get('path_color', path_color)
-                billboard_show = o['czml'].get('billboard_show', billboard_show)
-                if 'start_interval' in o['czml']:
-                    start_interval = time.to_astropy(time.utc_from_list_or_scalar(o['czml']['start_interval']))
-                if 'end_interval' in o['czml']:
-                    end_interval = time.to_astropy(time.utc_from_list_or_scalar(o['czml']['end_interval']))
-
-            extractor.add_object(sat, N=N, label_text=name, label_show=label_show,
-                                 start_interval=start_interval, end_interval=end_interval,
+
+            # czml
+            czml_config = o['czml'] if 'czml' in o else {}
+            label_show = czml_config.get('label_show', False)
+            path_show = czml_config.get('path_show', (ts_start_ob == t0))
+            path_color = czml_config.get('path_color', [255, 255, 0])
+            billboard_show = czml_config.get('billboard_show', True)
+            billboard_image = czml_config.get('billboard_image', PIC_SATELLITE)
+
+            start_interval = time.to_astropy(time.utc_from_list_or_scalar(o['czml']['start_interval'])) if 'start_interval' in czml_config else time.to_astropy(t0)
+            end_interval = time.to_astropy(time.utc_from_list_or_scalar(o['czml']['end_interval'])) if 'end_interval' in czml_config else time.to_astropy(t2)
+
+            extractor.add_object(sat, N=N, label_text=name, start_interval=start_interval, end_interval=end_interval,
                                  start_available=time.to_astropy(ts_start_ob), end_available=time.to_astropy(ts_end_ob),
-                                 path_show=path_show, id_name=name, billboard_show=billboard_show, path_color=path_color)
+                                 label_show=label_show, path_show=path_show, path_color=path_color, billboard_show=billboard_show,
+                                 billboard_image=billboard_image)
 
     # convert document to json
     j = extractor.get_document().dumps()
@@ -208,7 +211,11 @@ def add_ground_station(
         label_outline_color=[255, 255, 0, 255],
         label_font="16pt Lucida Console",
         label_text=None,
-        label_show=False
+        label_show=False,
+        billboard_show=True,
+        billboard_image=PIC_GROUNDSTATION,
+        cone_color=[255, 255, 0, 64],
+        cone_show=True,
     ):
         """
         Adds a ground station
@@ -235,7 +242,7 @@ def add_ground_station(
             label=Label(
                 show=True,
             ),
-            billboard=Billboard(image=PIC_GROUNDSTATION, show=True),
+            billboard=Billboard(image=billboard_image, show=True),
         )
 
         self.packets.append(pckt)
@@ -263,13 +270,13 @@ def add_ground_station(
                 backwardExtrapolationType=ExtrapolationTypes.EXTRAPOLATE
             ),
             agi_rectangularSensor=RectangularSensor(
-                show=True,
+                show=cone_show,
                 showIntersection=False,
                 intersectionColor=Color(rgba=[255, 255, 255, 255]),
                 intersectionWidth=2,
                 portionToDisplay="COMPLETE",
-                lateralSurfaceMaterial=Material(solidColor=SolidColorMaterial(color=Color(rgba=[255, 255, 0, 128]))),
-                domeSurfaceMaterial=Material(solidColor=SolidColorMaterial(color=Color(rgba=[255, 255, 0, 128]))),
+                lateralSurfaceMaterial=Material(solidColor=SolidColorMaterial(color=Color(rgba=cone_color))),
+                domeSurfaceMaterial=Material(solidColor=SolidColorMaterial(color=Color(rgba=cone_color))),
                 xHalfAngle=np.radians(sensor['x_fov'] / 2),
                 yHalfAngle=np.radians(sensor['y_fov'] / 2),
                 radius=Double(
@@ -302,7 +309,8 @@ def add_object(
         end_interval=None,
         start_available=None,
         end_available=None,
-        billboard_show=True
+        billboard_show=True,
+        billboard_image=PIC_SATELLITE,
     ):
         """
         Adds a SatSim Skyfield object
@@ -372,7 +380,7 @@ def add_object(
                 fillColor=Color(rgba=label_fill_color),
                 outlineColor=Color(rgba=label_outline_color),
             ),
-            billboard=Billboard(image=PIC_SATELLITE, show=billboard_show),
+            billboard=Billboard(image=billboard_image, show=billboard_show),
         )
 
         self.packets.append(pckt)

satsim/io/fits.py

@@ -17,7 +17,7 @@ def save(filename, fpa, exposure_time=0, dt_start=datetime.now(), header={}, ove
         filename: `string`, the FITS filename.
         fpa: `np.array`, input image as a 2D numpy array.
         exposure_time: `float`, exposure time for header.
-        dt_start: `datatime`, datetime of start of exposure.
+        dt_start: `datetime`, datetime of start of exposure.
         header: `dict`, placeholder, not implemented
         overwrite: `boolean`, if True overwrite file if it exists
         dtype: `string`, 'int16', 'uint16', 'int32', 'uint32', or 'float32'. default: 'int16'

satsim/io/satnet.py

@@ -6,6 +6,7 @@
 from collections import OrderedDict
 
 import numpy as np
+import tifffile
 
 from satsim.io import fits, image
 from satsim.config import save_json
@@ -156,7 +157,7 @@ def is_ob(a, b):
     return data
 
 
-def write_frame(dir_name, sat_name, fpa_digital, meta_data, frame_num, exposure_time, time_stamp, ssp, show_obs_boxes=True, astrometrics=None, save_pickle=False, dtype='uint16', save_jpeg=True):
+def write_frame(dir_name, sat_name, fpa_digital, meta_data, frame_num, exposure_time, time_stamp, ssp, show_obs_boxes=True, astrometrics=None, save_pickle=False, dtype='uint16', save_jpeg=True, ground_truth=None, ground_truth_min=0):
     """Write image and annotation files compatible with SatNet. In addition,
     writes annotated images and SatSim configuration file for reference.
 
@@ -170,6 +171,9 @@ def write_frame(dir_name, sat_name, fpa_digital, meta_data, frame_num, exposure_
         time_stamp: `datetime`, reference time
         ssp: `dict`: SatSim parameters to be saved to JSON file
         dtype: `string`: Data type to save FITS pixel data as
+        save_jpeg: `boolean`: specify to save a JPEG annotated image
+        ground_truth: `OrderedDict`: an ordered dictionary of arrays or numbers
+        ground_truth_min: `float`, set any value less than this number in ground_truth to 0
     """
 
     file_name = '{}.{:04d}'.format(sat_name, frame_num)
@@ -201,3 +205,20 @@ def write_frame(dir_name, sat_name, fpa_digital, meta_data, frame_num, exposure_
 
     # save sim config
     save_json(os.path.join(dir_name,'config.json'), ssp, save_pickle=save_pickle)
+
+    # save ground truth
+    if ground_truth is not None:
+
+        keys = ','.join(ground_truth.keys())
+
+        # broadcast scalars
+        def f(x):
+            return x if x.shape == fpa_digital.shape else np.resize(x, fpa_digital.shape)
+
+        ground_truth = np.stack(list(map(f, ground_truth.values())))
+
+        # clip values
+        ground_truth[ground_truth < ground_truth_min] = 0
+
+        tifffile.imwrite(os.path.join(annotation_dir, '{}.tiff'.format(file_name)), np.stack(ground_truth), dtype='float32', bigtiff=True, compression='lzw',
+                         metadata={'ImageDescription': keys})