Merge branch 'kubeedge:main' into TinyMS-Support-and-Demos

hack/run-e2e.sh

@@ -22,13 +22,19 @@ SEDNA_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd -P)"
 
 cd "$SEDNA_ROOT"
 
-# source local-up script
+# Prepare all-in-one env
 {
   __WITH_SOURCE__=true
   # this will export KUBECONFIG
-  source hack/local-up.sh
-  trap cleanup EXIT
+  echo "Prepare all-in-one env" 
+  cat scripts/installation/all-in-one.sh | KUBEEDGE_VERSION=v1.8.0 NUM_EDGE_NODES=0 bash -
 }
 
+# Running e2e
 echo "Running e2e..."
 go test ./test/e2e -v
+
+
+# Clean all-in-one env
+echo "Chean all-in-one env"
+cat scripts/installation/all-in-one.sh | bash /dev/stdin clean

lib/requirements.dev.txt

@@ -4,3 +4,4 @@ plato-learn~=0.26  # Apache-2.0
 scikit-learn~=0.24.1  # BSD
 # multi_edge_inference
 kafka-python~=2.0.2 # Apache-2.0
+opencv-python~=4.6.0 # Apache-2.0

lib/sedna/algorithms/__init__.py

@@ -16,5 +16,4 @@
 from . import hard_example_mining
 from . import multi_task_learning
 from . import unseen_task_detect
-from . import optical_flow
 from . import reid

lib/sedna/algorithms/optical_flow/__init__.py

@@ -14,9 +14,7 @@
 
 """Optical Flow Algorithms"""
 import abc
-
 import numpy
-import cv2
 from sedna.common.class_factory import ClassFactory, ClassType
 from sedna.common.log import LOGGER
 
@@ -40,6 +38,8 @@ def __call__(self, old_frame=None, current_frame=None):
 
 @ClassFactory.register(ClassType.OF, alias="LukasKanadeOF")
 class LukasKanade(BaseFilter, abc.ABC):
+    import cv2
+
     """
         Class to detect movement between two consecutive images.
     """
@@ -49,7 +49,7 @@ def __init__(self, **kwargs):
             dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
 
         # Parameters for Lucas Kanade optical flow
-        _criteria = cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT
+        _criteria = self.cv2.TERM_CRITERIA_EPS | self.cv2.TERM_CRITERIA_COUNT
 
         self.lk_params = dict(
             winSize=(15, 15),
@@ -67,14 +67,15 @@ def __call__(self, old_frame=None, current_frame=None):
 
         movement = False
         try:
-            old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
-            p0 = cv2.goodFeaturesToTrack(
+            old_gray = self.cv2.cvtColor(old_frame, self.cv2.COLOR_BGR2GRAY)
+            p0 = self.cv2.goodFeaturesToTrack(
                 old_gray, mask=None, **self.feature_params)
 
-            current_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
+            current_gray = \
+                self.cv2.cvtColor(current_frame, self.cv2.COLOR_BGR2GRAY)
 
             # Calculate Optical Flow
-            p1, st, err = cv2.calcOpticalFlowPyrLK(
+            p1, st, err = self.cv2.calcOpticalFlowPyrLK(
                 old_gray, current_gray, p0, None, **self.lk_params
             )
 
@@ -88,7 +89,10 @@ def __call__(self, old_frame=None, current_frame=None):
             # Allclose is used instead of array_equal to support
             # array of floats (if we remove rounding).
             movement = \
-                not numpy.allclose(numpy.rint(good_new), numpy.rint(good_old))
+                not numpy.allclose(
+                    numpy.rint(good_new),
+                    numpy.rint(good_old)
+                )
         except Exception as ex:
             LOGGER.error(
                 f"Error during the execution of\
@@ -99,6 +103,9 @@ def __call__(self, old_frame=None, current_frame=None):
 
 @ClassFactory.register(ClassType.OF, alias="LukasKanadeOF_CUDA")
 class LukasKanadeCUDA(BaseFilter, abc.ABC):
+    import cv2
+    import numpy
+
     """
         Class to detect movement between
         two consecutive images (GPU implementation).
@@ -107,7 +114,7 @@ def __init__(self, **kwargs):
         # Parameters for ShiTomasi corner detection
         self.feature_params = \
             dict(
-                srcType=cv2.CV_8UC1,
+                srcType=self.cv2.CV_8UC1,
                 maxCorners=100,
                 qualityLevel=0.3,
                 minDistance=7,
@@ -120,8 +127,11 @@ def __init__(self, **kwargs):
         )
 
         self.corner_detector = \
-            cv2.cuda.createGoodFeaturesToTrackDetector(**self.feature_params)
-        self.of = cv2.cuda.SparsePyrLKOpticalFlow_create(**self.lk_params)
+            self.cv2.cuda.createGoodFeaturesToTrackDetector(
+                **self.feature_params
+            )
+        self.of = \
+            self.cv2.cuda.SparsePyrLKOpticalFlow_create(**self.lk_params)
 
     def __call__(self, old_frame=None, current_frame=None):
         """
@@ -131,16 +141,17 @@ def __call__(self, old_frame=None, current_frame=None):
              `False` means that there is no movement.
         """
 
-        old_frame = cv2.cuda_GpuMat(old_frame)
-        current_frame = cv2.cuda_GpuMat(current_frame)
+        old_frame = self.cv2.cuda_GpuMat(old_frame)
+        current_frame = self.cv2.cuda_GpuMat(current_frame)
 
         movement = False
         try:
-            old_gray = cv2.cuda.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
+            old_gray = \
+                self.cv2.cuda.cvtColor(old_frame, self.cv2.COLOR_BGR2GRAY)
             p0 = self.corner_detector.detect(old_gray)
 
             current_gray = \
-                cv2.cuda.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
+                self.cv2.cuda.cvtColor(current_frame, self.cv2.COLOR_BGR2GRAY)
 
             # Calculate Optical Flow
             p1, st, err = self.of.calc(
@@ -161,7 +172,10 @@ def __call__(self, old_frame=None, current_frame=None):
             # Allclose is used instead of array_equal to
             # support array of floats (if we remove rounding).
             movement = \
-                not numpy.allclose(numpy.rint(good_new), numpy.rint(good_old))
+                not numpy.allclose(
+                    numpy.rint(good_new),
+                    numpy.rint(good_old)
+                )
         except Exception as ex:
             LOGGER.error(
                 f"Error during the execution of\