Improvements in tests, bugfix in digits_video sample
This commit is contained in:
Vladislav Sovrasov
parent
4e3a6328ba
commit
87fc75c6d7
@ -24,32 +24,22 @@ import numpy as np
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import cv2
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from tst_scene_render import TestSceneRender
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def intersectionRate(s1, s2):
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x1, y1, x2, y2 = s1
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s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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x1, y1, x2, y2 = s2
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s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
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return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
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from tests_common import NewOpenCVTests
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from tests_common import NewOpenCVTests, intersectionRate
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class camshift_test(NewOpenCVTests):
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framesNum = 300
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frame = None
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selection = None
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drag_start = None
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show_backproj = False
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track_window = None
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render = None
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errors = 0
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def prepareRender(self):
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self.render = TestSceneRender(self.get_sample('samples/data/pca_test1.jpg'))
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self.render = TestSceneRender(self.get_sample('samples/data/pca_test1.jpg'), True)
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def runTracker(self):
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@ -93,16 +83,18 @@ class camshift_test(NewOpenCVTests):
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if self.show_backproj:
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vis[:] = prob[...,np.newaxis]
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trackingRect = np.array(self.track_window)
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trackingRect[2] += trackingRect[0]
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trackingRect[3] += trackingRect[1]
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self.assertGreater(intersectionRate((self.render.getCurrentRect()), trackingRect), 0.5)
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if intersectionRate(self.render.getCurrentRect(), trackingRect) < 0.4:
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self.errors += 1
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if framesCounter > 300:
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if framesCounter > self.framesNum:
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break
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self.assertLess(float(self.errors) / self.framesNum, 0.4)
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def test_camshift(self):
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self.prepareRender()
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self.runTracker()
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@ -95,7 +95,7 @@ def evaluate_model(model, digits, samples, labels):
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confusion = np.zeros((10, 10), np.int32)
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for i, j in zip(labels, resp):
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confusion[i, j] += 1
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confusion[int(i), int(j)] += 1
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return err, confusion
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@ -10,17 +10,6 @@ from __future__ import print_function
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import numpy as np
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import cv2
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def intersectionRate(s1, s2):
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x1, y1, x2, y2 = s1
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s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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x1, y1, x2, y2 = s2
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s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
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return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
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def detect(img, cascade):
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rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
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flags=cv2.CASCADE_SCALE_IMAGE)
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@ -29,7 +18,7 @@ def detect(img, cascade):
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rects[:,2:] += rects[:,:2]
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return rects
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from tests_common import NewOpenCVTests
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from tests_common import NewOpenCVTests, intersectionRate
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class facedetect_test(NewOpenCVTests):
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@ -10,9 +10,31 @@ from __future__ import print_function
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import cv2
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import numpy as np
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import sys
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from numpy import pi, sin, cos
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from tests_common import NewOpenCVTests
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def circleApproximation(circle):
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nPoints = 30
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phi = 0
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dPhi = 2*pi / nPoints
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contour = []
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for i in range(nPoints):
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contour.append(([circle[0] + circle[2]*cos(i*dPhi),
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circle[1] + circle[2]*sin(i*dPhi)]))
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return np.array(contour).astype(int)
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def convContoursIntersectiponRate(c1, c2):
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s1 = cv2.contourArea(c1)
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s2 = cv2.contourArea(c2)
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s, _ = cv2.intersectConvexConvex(c1, c2)
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return 2*s/(s1+s2)
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class houghcircles_test(NewOpenCVTests):
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def test_houghcircles(self):
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@ -45,13 +67,15 @@ class houghcircles_test(NewOpenCVTests):
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[448.4, 121.3, 9.12],
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[384.6, 128.9, 8.62]]
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eps = 7
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matches_counter = 0
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for i in range(len(testCircles)):
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for j in range(len(circles)):
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if cv2.norm(testCircles[i] - circles[j], cv2.NORM_L2) < eps:
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tstCircle = circleApproximation(testCircles[i])
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circle = circleApproximation(circles[j])
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if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
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matches_counter += 1
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self.assertGreater(float(matches_counter) / len(testCircles), .5)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .7)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
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@ -16,18 +16,7 @@ def inside(r, q):
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qx, qy, qw, qh = q
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return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
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def intersectionRate(s1, s2):
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x1, y1, x2, y2 = s1
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s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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x1, y1, x2, y2 = s2
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s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
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return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
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from tests_common import NewOpenCVTests
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from tests_common import NewOpenCVTests, intersectionRate
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class peopledetect_test(NewOpenCVTests):
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def test_peopledetect(self):
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@ -53,4 +53,15 @@ class NewOpenCVTests(unittest.TestCase):
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def assertGreater(self, a, b, msg=None):
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if not a > b:
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self.fail('%s not greater than %s' % (repr(a), repr(b)))
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self.fail('%s not greater than %s' % (repr(a), repr(b)))
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def intersectionRate(s1, s2):
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x1, y1, x2, y2 = s1
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s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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x1, y1, x2, y2 = s2
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s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
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#print(np.array(s2))
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area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
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return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
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@ -13,9 +13,10 @@ defaultSize = 512
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class TestSceneRender():
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def __init__(self, bgImg = None, **params):
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def __init__(self, bgImg = None, deformation = False, **params):
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self.time = 0.0
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self.timeStep = 1.0 / 30.0
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self.deformation = deformation
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if bgImg != None:
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self.sceneBg = bgImg.copy()
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@ -26,7 +27,7 @@ class TestSceneRender():
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self.h = self.sceneBg.shape[1]
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self.initialRect = np.array([ (self.h/2, self.w/2), (self.h/2, self.w/2 + self.w/10),
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(self.h/2 + self.h/10, self.w/2 + self.w/10), (self.h/2 + self.h/10, self.w/2)])
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(self.h/2 + self.h/10, self.w/2 + self.w/10), (self.h/2 + self.h/10, self.w/2)]).astype(int)
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self.currentRect = self.initialRect
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def setInitialRect(self, rect):
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@ -42,6 +43,9 @@ class TestSceneRender():
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img = self.sceneBg.copy()
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self.currentRect = self.initialRect + np.int( 30*cos(self.time) + 50*sin(self.time/3))
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if(self.deformation):
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self.currentRect[1:3] += np.int(self.h/20*cos(self.time))
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cv2.fillConvexPoly(img, self.currentRect, (0, 0, 255))
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return img
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@ -27,9 +27,12 @@ def main():
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if not os.path.exists(classifier_fn):
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print('"%s" not found, run digits.py first' % classifier_fn)
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return
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model = SVM()
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model.load(classifier_fn)
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if True:
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model = cv2.ml.SVM_load(classifier_fn)
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else:
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model = cv2.ml.SVM_create()
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model.load_(classifier_fn) #Known bug: https://github.com/Itseez/opencv/issues/4969
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while True:
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ret, frame = cap.read()
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