Merge branch 'master' of https://github.com/superannotateai/superannotate-python-sdk

Author: agyurjin

.vscode/settings.json

@@ -1,4 +1,5 @@
 {
-    "python.pythonPath": "~/miniconda3/bin/python",
-    "python.formatting.provider": "yapf"
-}
+    "python.formatting.provider": "yapf",
+    "python.linting.pylintPath": "~/miniconda3/bin/pylint",
+    "python.linting.pylintEnabled": true
+}

install.sh

@@ -11,6 +11,7 @@ rm -rf venv_sa_conv
 python${PYTHON_VER} -m venv venv_sa_conv
 source venv_sa_conv/bin/activate
 
+pip install shapely
 pip install -e .
 # pip install --pre superannotate
 # pip install superannotate

superannotate/__init__.py

@@ -22,7 +22,17 @@
     from .consensus_benchmark.benchmark import benchmark
     from .consensus_benchmark.consensus import consensus
 except:
-    pass
+
+    def benchmark(*args, **kwargs):
+        raise RuntimeError(
+            "To use superannotate.benchmark or superannotate.consensus functions please install shapely package with # conda install shapely or # pip install shapely"
+        )
+
+    def consensus(*args, **kwargs):
+        raise RuntimeError(
+            "To use superannotate.benchmark or superannotate.consensus functions please install shapely package with # conda install shapely or # pip install shapely"
+        )
+
 
 from .dataframe_filtering import (
     filter_annotation_instances, filter_images_by_comments,

superannotate/db/projects.py

@@ -249,43 +249,44 @@ def upload_video_to_project(
                 rot
             )
     except Exception as e:
-        logger.warning("Couldn't read video metadata. %s", e)
+        logger.warning("Couldn't read video metadata %s", e)
 
     video = cv2.VideoCapture(str(video_path), cv2.CAP_FFMPEG)
     if not video.isOpened():
         raise SABaseException(0, "Couldn't open video file " + str(video_path))
 
     total_num_of_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
     if total_num_of_frames < 0:
-        if target_fps is not None:
-            logger.warning(
-                "Number of frames indicated in the video is negative number. Disabling FPS change."
-            )
-            target_fps = None
-    else:
-        logger.info("Video frame count is %s.", total_num_of_frames)
+        total_num_of_frames = 0
+        flag = True
+        while flag:
+            flag, frame = video.read()
+            if flag:
+                total_num_of_frames += 1
+            else:
+                break
+        video = cv2.VideoCapture(str(video_path), cv2.CAP_FFMPEG)
+    logger.info("Video frame count is %s.", total_num_of_frames)
 
     if target_fps is not None:
-        video_fps = video.get(cv2.CAP_PROP_FPS)
+        video_fps = float(video.get(cv2.CAP_PROP_FPS))
         logger.info(
             "Video frame rate is %s. Target frame rate is %s.", video_fps,
             target_fps
         )
-        if target_fps > video_fps:
+        if target_fps >= video_fps:
             target_fps = None
         else:
             r = video_fps / target_fps
-            frames_count_to_drop = total_num_of_frames - (
-                total_num_of_frames / r
-            )
-            percent_to_drop = frames_count_to_drop / total_num_of_frames
+            percent_to_drop = 1.0 - 1.0 / r
             my_random = random.Random(122222)
 
     zero_fill_count = len(str(total_num_of_frames))
     tempdir = tempfile.TemporaryDirectory()
 
     video_name = Path(video_path).stem
     frame_no = 1
+    logger.info("Extracting frames from video to %s.", tempdir.name)
     while True:
         success, frame = video.read()
         if not success:

superannotate/version.py

@@ -1 +1 @@
-__version__ = "2.5.9"
+__version__ = "2.5.12"

tests/consensus_benchmark/test_benchmark.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from superannotate.consensus_benchmark.benchmark import benchmark
+import superannotate as sa
 
 test_root = Path().resolve() / 'tests'
 
@@ -15,7 +15,7 @@ def test_benchmark():
     ]
     export_path = test_root / 'consensus_benchmark'
     for annot_type in annot_types:
-        res_df = benchmark(
+        res_df = sa.benchmark(
             gt_project_name,
             project_names,
             export_root=export_path,
@@ -39,7 +39,7 @@ def test_benchmark():
     ]
 
     #test filtering images with given image names list
-    res_images = benchmark(
+    res_images = sa.benchmark(
         gt_project_name,
         project_names,
         export_root=export_path,

tests/consensus_benchmark/test_consensus.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from superannotate.consensus_benchmark.consensus import consensus
+import superannotate as sa
 
 test_root = Path().resolve() / 'tests'
 
@@ -14,7 +14,7 @@ def test_consensus():
     ]
     export_path = test_root / 'consensus_benchmark'
     for annot_type in annot_types:
-        res_df = consensus(
+        res_df = sa.consensus(
             project_names, export_root=export_path, annot_type=annot_type
         )
         #test content of projectName column
@@ -35,7 +35,7 @@ def test_consensus():
     ]
 
     #test filtering images with given image names list
-    res_images = consensus(
+    res_images = sa.consensus(
         project_names, export_root=export_path, image_list=image_names
     )
 

tests/test_dicom.py

@@ -7,7 +7,5 @@ def test_dicom_convesion(tmpdir):
     print("Using temp dir", tmpdir)
     path = pydicom.data.get_testdata_file("CT_small.dcm")
     print("Using dicom file", path)
-    paths = sa.dicom_to_rgb_sequence(
-        path, tmpdir
-    )
+    paths = sa.dicom_to_rgb_sequence(path, tmpdir)
     assert len(paths) == 1