def test_peopledetect(self):
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
dirPath = 'samples/gpu/'
samples = ['basketball1.png', 'basketball2.png']
testPeople = [[[23, 76, 164, 477], [440, 22, 637, 478]],
[[23, 76, 164, 477], [440, 22, 637, 478]]]
eps = 0.5
for sample in samples:
img = self.get_sample(dirPath + sample, 0)
found, w = hog.detectMultiScale(img,
winStride=(8, 8),
padding=(32, 32),
scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
matches = 0
for i in range(len(found_filtered)):
for j in range(len(testPeople)):
found_rect = (found_filtered[i][0], found_filtered[i][1],
found_filtered[i][0] + found_filtered[i][2],
found_filtered[i][1] + found_filtered[i][3])
if intersectionRate(
found_rect,
testPeople[j][0]) > eps or intersectionRate(
found_rect, testPeople[j][1]) > eps:
matches += 1
self.assertGreater(matches, 0)
def test_peopledetect(self):
hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
dirPath = 'samples/data/'
samples = ['basketball1.png', 'basketball2.png']
testPeople = [
[[23, 76, 164, 477], [440, 22, 637, 478]],
[[23, 76, 164, 477], [440, 22, 637, 478]]
]
eps = 0.5
for sample in samples:
img = self.get_sample(dirPath + sample, 0)
found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
matches = 0
for i in range(len(found_filtered)):
for j in range(len(testPeople)):
found_rect = (found_filtered[i][0], found_filtered[i][1],
found_filtered[i][0] + found_filtered[i][2],
found_filtered[i][1] + found_filtered[i][3])
if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps:
matches += 1
self.assertGreater(matches, 0)
def runTracker(self):
framesCounter = 0
self.selection = True
xmin, ymin, xmax, ymax = self.render.getCurrentRect()
self.track_window = (xmin, ymin, xmax - xmin, ymax - ymin)
while True:
framesCounter += 1
self.frame = self.render.getNextFrame()
hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.render.getCurrentRect() + 50
x0 -= 100
y0 -= 100
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv2.calcHist([hsv_roi], [0], mask_roi, [16], [0, 180])
cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
self.hist = hist.reshape(-1)
self.selection = False
if self.track_window and self.track_window[
2] > 0 and self.track_window[3] > 0:
self.selection = None
prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
10, 1)
track_box, self.track_window = cv2.CamShift(
prob, self.track_window, term_crit)
trackingRect = np.array(self.track_window)
trackingRect[2] += trackingRect[0]
trackingRect[3] += trackingRect[1]
if intersectionRate(self.render.getCurrentRect(),
trackingRect) < 0.4:
self.errors += 1
if framesCounter > self.framesNum:
break
self.assertLess(float(self.errors) / self.framesNum, 0.4)
def runTracker(self):
framesCounter = 0
self.selection = True
xmin, ymin, xmax, ymax = self.render.getCurrentRect()
self.track_window = (xmin, ymin, xmax - xmin, ymax - ymin)
while True:
framesCounter += 1
self.frame = self.render.getNextFrame()
hsv = cv.cvtColor(self.frame, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.render.getCurrentRect() + 50
x0 -= 100
y0 -= 100
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX)
self.hist = hist.reshape(-1)
self.selection = False
if self.track_window and self.track_window[2] > 0 and self.track_window[3] > 0:
self.selection = None
prob = cv.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
_track_box, self.track_window = cv.CamShift(prob, self.track_window, term_crit)
trackingRect = np.array(self.track_window)
trackingRect[2] += trackingRect[0]
trackingRect[3] += trackingRect[1]
if intersectionRate(self.render.getCurrentRect(), trackingRect) < 0.4:
self.errors += 1
if framesCounter > self.framesNum:
break
self.assertLess(float(self.errors) / self.framesNum, 0.4)
def test_facedetect(self):
cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
nested_fn = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
cascade = cv.CascadeClassifier(cascade_fn)
nested = cv.CascadeClassifier(nested_fn)
samples = [
'samples/data/lena.jpg', 'cv/cascadeandhog/images/mona-lisa.png'
]
faces = []
eyes = []
testFaces = [
#lena
[[218, 200, 389, 371], [244, 240, 294, 290], [309, 246, 352, 289]],
#lisa
[[167, 119, 307, 259], [188, 153, 229, 194], [236, 153, 277, 194]]
]
for sample in samples:
img = self.get_sample(sample)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.GaussianBlur(gray, (5, 5), 5.1)
rects = detect(gray, cascade)
faces.append(rects)
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
for rect in subrects:
rect[0] += x1
rect[2] += x1
rect[1] += y1
rect[3] += y1
eyes.append(subrects)
faces_matches = 0
eyes_matches = 0
eps = 0.8
for i in range(len(faces)):
for j in range(len(testFaces)):
if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
faces_matches += 1
#check eyes
if len(eyes[i]) == 2:
if intersectionRate(
eyes[i][0],
testFaces[j][1]) > eps and intersectionRate(
eyes[i][1], testFaces[j][2]) > eps:
eyes_matches += 1
elif intersectionRate(
eyes[i][1],
testFaces[j][1]) > eps and intersectionRate(
eyes[i][0], testFaces[j][2]) > eps:
eyes_matches += 1
self.assertEqual(faces_matches, 2)
self.assertEqual(eyes_matches, 2)
def test_facedetect(self):
import sys, getopt
cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
nested_fn = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
samples = ['samples/data/lena.jpg', 'samples/data/cv/cascadeandhog/images/mona-lisa.png']
faces = []
eyes = []
testFaces = [
#lena
[[218, 200, 389, 371],
[ 244, 240, 294, 290],
[ 309, 246, 352, 289]],
#lisa
[[167, 119, 307, 259],
[188, 153, 229, 194],
[236, 153, 277, 194]]
]
for sample in samples:
img = self.get_sample( sample)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 5.1)
rects = detect(gray, cascade)
faces.append(rects)
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
for rect in subrects:
rect[0] += x1
rect[2] += x1
rect[1] += y1
rect[3] += y1
eyes.append(subrects)
faces_matches = 0
eyes_matches = 0
eps = 0.8
for i in range(len(faces)):
for j in range(len(testFaces)):
if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
faces_matches += 1
#check eyes
if len(eyes[i]) == 2:
if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1] , testFaces[j][2]) > eps:
eyes_matches += 1
elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]) > eps:
eyes_matches += 1
self.assertEqual(faces_matches, 2)
self.assertEqual(eyes_matches, 2)
