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Add high level API (Merge pull request #14780)

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* Add high level API

* Fix Model

* Add DetectionModel

* Add ClassificationModel

* Fix classify

* Add python test

* Fix pytest

* Fix comments to review

* Fix detect

* Fix docs

* Modify DetectionOutput postprocessing

* Fix test

* Extract ref boxes

* Fix draw rect

* fix test

* Add rect wrap

* Fix wrap

* Fix detect

* Fix Rect wrap

* Fix OCL_FP16

* Fix MyriadX

* Fix nms

* Fix NMS

* Fix coords
This commit is contained in:
Lubov Batanina
2019-07-30 23:07:37 +03:00
committed by Maksim Shabunin
parent f482050f9a
commit 778f42ad34
5 changed files with 685 additions and 17 deletions
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modules/dnn/misc/python/test/test_dnn.py
+42 -8
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@@ -21,15 +21,11 @@ def box2str(box):
width, height = box[2] - left, box[3] - top
return '[%f x %f from (%f, %f)]' % (width, height, left, top)
def normAssertDetections(test, ref, out, confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4):
ref = np.array(ref, np.float32)
refClassIds, testClassIds = ref[:, 1], out[:, 1]
refScores, testScores = ref[:, 2], out[:, 2]
refBoxes, testBoxes = ref[:, 3:], out[:, 3:]
def normAssertDetections(test, refClassIds, refScores, refBoxes, testClassIds, testScores, testBoxes,
confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4):
matchedRefBoxes = [False] * len(refBoxes)
errMsg = ''
for i in range(len(refBoxes)):
for i in range(len(testBoxes)):
testScore = testScores[i]
if testScore < confThreshold:
continue
@@ -136,6 +132,38 @@ class dnn_test(NewOpenCVTests):
normAssert(self, blob, target)
def test_model(self):
img_path = self.find_dnn_file("dnn/street.png")
weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel")
config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt")
frame = cv.imread(img_path)
model = cv.dnn_DetectionModel(weights, config)
size = (300, 300)
mean = (127.5, 127.5, 127.5)
scale = 1.0 / 127.5
model.setInputParams(size=size, mean=mean, scale=scale)
iouDiff = 0.05
confThreshold = 0.0001
nmsThreshold = 0
scoreDiff = 1e-3
classIds, confidences, boxes = model.detect(frame, confThreshold, nmsThreshold)
refClassIds = (7, 15)
refConfidences = (0.9998, 0.8793)
refBoxes = ((328, 238, 85, 102), (101, 188, 34, 138))
normAssertDetections(self, refClassIds, refConfidences, refBoxes,
classIds, confidences, boxes,confThreshold, scoreDiff, iouDiff)
for box in boxes:
cv.rectangle(frame, box, (0, 255, 0))
cv.rectangle(frame, np.array(box), (0, 255, 0))
cv.rectangle(frame, tuple(box), (0, 255, 0))
cv.rectangle(frame, list(box), (0, 255, 0))
def test_face_detection(self):
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required)
@@ -166,7 +194,13 @@ class dnn_test(NewOpenCVTests):
scoresDiff = 4e-3 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-5
iouDiff = 2e-2 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-4
normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
ref = np.array(ref, np.float32)
refClassIds, testClassIds = ref[:, 1], out[:, 1]
refScores, testScores = ref[:, 2], out[:, 2]
refBoxes, testBoxes = ref[:, 3:], out[:, 3:]
normAssertDetections(self, refClassIds, refScores, refBoxes, testClassIds,
testScores, testBoxes, 0.5, scoresDiff, iouDiff)
def test_async(self):
timeout = 500*10**6 # in nanoseconds (500ms)