def get_set(metadataFile, classType):
set = []
with open(metadataFile, "r") as f:
entries = f.readlines()
for entry in entries:
entry = entry.split()
filePath = entry[0]
x, y, scale = int(entry[1]), int(entry[2]), float(entry[3])
img = cv2.imread(filePath)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray_crop = img_gray[y:y + 128, x:x + 64]
hog_gray = hog(img_gray_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
prevFilePath = utils.get_prev_img(filePath)
prev_img = cv2.imread(prevFilePath)
prev_img = cv2.resize(prev_img, (0, 0), fx=scale, fy=scale)
prev_img_gray = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prev_img_gray, img_gray, 0.5, 3,
15, 3, 5, 1.2, 0)
# flowx, flowy = flow[..., 0], flow[..., 1]
# flowx_crop, flowy_crop = flowx[y:y+128, x:x+64], flowy[y:y+128, x:x+64]
#
# hog_flow_x = hog(flowx_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
# hog_flow_y = hog(flowy_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
hsv = numpy.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / numpy.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_gray = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
flow_gray_crop = flow_gray[y:y + 128, x:x + 64]
hog_flow = hog(flow_gray_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
desc = hog_gray + hog_flow
set.append(desc)
return set, [classType] * len(entries)
def test_img(svm, img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
windows_features = []
sc = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3, 5, 1.2, 0)
hsv = np.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180/ np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_bw = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
flow_crop = flow_bw[y:y + 128, x:x + 64]
fd_flow = hog(flow_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
fd = hog_gray + fd_flow
windows.append((x, y))
windows_features.append(fd)
sc.append(scale)
classes = svm.predict(windows_features)
results = []
for i in range(0, len(windows)):
if classes[i] == 1:
scale = sc[i]
results.append((int(windows[i][0] / scale), int(windows[i][1] / scale), int(64 / scale), int(128 / scale)))
return results
def getWindowsAndDescriptors(img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3,
5, 1.2, 0)
hsv = np.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_bw = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(
subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
flow_crop = flow_bw[y:y + 128, x:x + 64]
fd_flow = hog(flow_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
fd = hog_gray + fd_flow
windows.append([(int(x / scale), int(y / scale),
int(64 / scale), int(128 / scale)), fd])
return windows
def test_img_new(svm, img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
windows_features = []
sc = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3, 5, 1.2, 0)
flowx, flowy = flow[..., 0], flow[..., 1]
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
flowx_crop, flowy_crop = flowx[y:y+128, x:x+64], flowy[y:y+128, x:x+64]
hog_flow_x = hog(flowx_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
hog_flow_y = hog(flowy_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
fd = numpy.concatenate((hog_gray, hog_flow_x, hog_flow_y))
windows.append((x, y))
windows_features.append(fd)
sc.append(scale)
classes = svm.predict(windows_features)
results = []
for i in range(0, len(windows)):
if classes[i] == 1:
scale = sc[i]
results.append((int(windows[i][0] / scale), int(windows[i][1] / scale), int(64 / scale), int(128 / scale)))
return results
def get_set(metadataFile, classType):
set = []
with open(metadataFile, "r") as f:
entries = f.readlines()
for entry in entries:
entry = entry.split()
filePath = entry[0]
x, y, scale = int(entry[1]), int(entry[2]), float(entry[3])
img = cv2.imread(filePath)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray_crop = img_gray[y:y+128, x:x+64]
hog_gray = hog(img_gray_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
prevFilePath = utils.get_prev_img(filePath)
prev_img = cv2.imread(prevFilePath)
prev_img = cv2.resize(prev_img, (0, 0), fx=scale, fy=scale)
prev_img_gray = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prev_img_gray, img_gray, 0.5, 3, 15, 3, 5, 1.2, 0)
# flowx, flowy = flow[..., 0], flow[..., 1]
# flowx_crop, flowy_crop = flowx[y:y+128, x:x+64], flowy[y:y+128, x:x+64]
#
# hog_flow_x = hog(flowx_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
# hog_flow_y = hog(flowy_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
hsv = numpy.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180/ numpy.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_gray = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
flow_gray_crop = flow_gray[y:y+128, x:x+64]
hog_flow = hog(flow_gray_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
desc = hog_gray + hog_flow
set.append(desc)
return set, [classType] * len(entries)
def getDecisionFunctionsForWindows(checkerFile,
imageDirs,
classifier,
thresh,
hog=True,
subwindow=False):
checker = detection_checker.Checker(checkerFile)
fileLists = checker.getFileList()
metadata = utils.parseMetadata(*imageDirs)
trueClasses = []
scores = numpy.array([])
scales = [[0.45, 0.5, 0.55], [0.4, 0.45, 0.5], [0.3, 0.35], [0.3]]
scaleSteps = [35, 45, 65, 90]
index = 0
for imgPath in fileLists[:10]:
print index
index += 1
tilt = int(metadata[imgPath]['tilt'])
if tilt > 90:
tilt = 90 - (tilt - 90)
imgScales = []
for i in range(0, len(scaleSteps)):
if tilt < scaleSteps[i]:
imgScales = scales[i]
break
boundingRect = None
if subwindow:
boundingRect = optical_flow.optical_flow(
imgPath, utils.get_prev_img(imgPath))
if hog:
wf = tester_hog.getWindowsAndDescriptors(imgPath,
imgScales,
subwindow=boundingRect)
else:
wf = cascade_tester.get_images(classifier,
imgPath,
imgScales,
subwindow=boundingRect)
tc = checker.getWindowsClasses(imgPath, [w[0] for w in wf])
df = computeClassifierDecisions(wf, classifier, thresh, hog=hog)
print df
trueClasses = trueClasses + tc
scores = numpy.concatenate((scores, df))
return trueClasses, scores
def test_img_new(svm, img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
windows_features = []
sc = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3,
5, 1.2, 0)
flowx, flowy = flow[..., 0], flow[..., 1]
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(
subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
flowx_crop, flowy_crop = flowx[y:y + 128,
x:x + 64], flowy[y:y + 128,
x:x + 64]
hog_flow_x = hog(flowx_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
hog_flow_y = hog(flowy_crop,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualise=False)
fd = numpy.concatenate((hog_gray, hog_flow_x, hog_flow_y))
windows.append((x, y))
windows_features.append(fd)
sc.append(scale)
classes = svm.predict(windows_features)
results = []
for i in range(0, len(windows)):
if classes[i] == 1:
scale = sc[i]
results.append(
(int(windows[i][0] / scale), int(windows[i][1] / scale),
int(64 / scale), int(128 / scale)))
return results
def test_multiscale(hog_classifier_file, icf_classifier_file, hog_result_dir,
icf_result_dir, no_samples):
hog_classifier = tester_hog.load_classifier(hog_classifier_file)
icf_classifier = tester_icf.load_classifier(icf_classifier_file)
filepaths = [
"/home/mataevs/captures/metadata/dump_05_05_01_50",
"/home/mataevs/captures/metadata/dump_05_06_13_10",
"/home/mataevs/captures/metadata/dump_10_06_11_47",
"/home/mataevs/captures/metadata/dump_05_05_01_51",
"/home/mataevs/captures/metadata/dump_05_06_13_15",
"/home/mataevs/captures/metadata/dump_07_05_11_40",
"/home/mataevs/captures/metadata/dump_10_06_11_48",
"/home/mataevs/captures/metadata/dump_05_05_11_54",
"/home/mataevs/captures/metadata/dump_05_06_13_20",
"/home/mataevs/captures/metadata/dump_07_05_11_46",
"/home/mataevs/captures/metadata/dump_10_06_12_16",
"/home/mataevs/captures/metadata/dump_05_06_12_57",
"/home/mataevs/captures/metadata/dump_05_06_13_21",
"/home/mataevs/captures/metadata/dump_05_06_13_24",
"/home/mataevs/captures/metadata/dump_16_06_14_57",
"/home/mataevs/captures/metadata/dump_05_06_13_25",
"/home/mataevs/captures/metadata/dump_07_05_12_03",
"/home/mataevs/captures/metadata/dump_16_06_15_26",
"/home/mataevs/captures/metadata/dump_05_06_13_28",
"/home/mataevs/captures/metadata/dump_07_05_12_05"
]
if not os.path.exists(hog_result_dir):
os.makedirs(hog_result_dir)
if not os.path.exists(icf_result_dir):
os.makedirs(icf_result_dir)
testImages = utils.getFullImages(*filepaths)
metadata = utils.parseMetadata(*filepaths)
scales = [[0.45, 0.5, 0.55], [0.4, 0.45, 0.5], [0.3, 0.35], [0.3]]
scaleSteps = [35, 45, 65, 90]
for sample in range(0, no_samples):
print "### Sample " + str(sample) + " ###"
imgPath = random.choice(testImages)
img = cv2.imread(imgPath)
tilt = int(metadata[imgPath]['tilt'])
if tilt > 90:
tilt = 90 - (tilt - 90)
imgScales = []
for i in range(0, len(scaleSteps)):
if tilt < scaleSteps[i]:
imgScales = scales[i]
break
print imgScales
prev_img_path = utils.get_prev_img(imgPath)
prev_img = cv2.imread(prev_img_path)
flow_rgb, boundingRect = optical_flow.optical_flow(img, prev_img)
height, width, _ = img.shape
bestWindowsHog = tester_hog.test_img(hog_classifier,
imgPath,
imgScales,
allPositive=True,
flow_rgb=flow_rgb,
subwindow=boundingRect)
if bestWindowsHog != None and bestWindowsHog != []:
scale = bestWindowsHog[0][4]
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect,
img_hog.shape[1],
img_hog.shape[0], scale)
cv2.rectangle(img_hog, (x, y), (x + w, y + h), (0, 0, 255),
thickness=2,
lineType=8)
utils.draw_detections(img_hog, bestWindowsHog)
else:
scale = 0.5
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect,
img_hog.shape[1],
img_hog.shape[0], scale)
cv2.rectangle(img_hog, (x, y), (x + w, y + h), (0, 0, 255),
thickness=2,
lineType=8)
cv2.imwrite(hog_result_dir + "/sample_2_" + str(sample) + ".jpg",
img_hog)
bestWindowsIcf = tester_icf.test_img(icf_classifier,
imgPath,
imgScales,
allPositive=True,
subwindow=boundingRect)
if bestWindowsIcf != None and bestWindowsIcf != []:
scale = bestWindowsIcf[0][4]
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect,
img_icf.shape[1],
img_icf.shape[0], scale)
cv2.rectangle(img_icf, (x, y), (x + w, y + h), (0, 0, 255),
thickness=2,
lineType=8)
utils.draw_detections(img_icf, bestWindowsIcf)
else:
scale = 0.5
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect,
img_icf.shape[1],
img_icf.shape[0], scale)
cv2.rectangle(img_icf, (x, y), (x + w, y + h), (0, 0, 255),
thickness=2,
lineType=8)
cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg",
img_icf)
def test_multiscale(
hog_classifier_file,
icf_classifier_file,
hog_result_dir,
icf_result_dir,
no_samples):
hog_classifier = tester_hog.load_classifier(hog_classifier_file)
icf_classifier = tester_icf.load_classifier(icf_classifier_file)
filepaths = [
"/home/mataevs/captures/metadata/dump_05_05_01_50",
"/home/mataevs/captures/metadata/dump_05_06_13_10",
"/home/mataevs/captures/metadata/dump_10_06_11_47",
"/home/mataevs/captures/metadata/dump_05_05_01_51",
"/home/mataevs/captures/metadata/dump_05_06_13_15",
"/home/mataevs/captures/metadata/dump_07_05_11_40",
"/home/mataevs/captures/metadata/dump_10_06_11_48",
"/home/mataevs/captures/metadata/dump_05_05_11_54",
"/home/mataevs/captures/metadata/dump_05_06_13_20",
"/home/mataevs/captures/metadata/dump_07_05_11_46",
"/home/mataevs/captures/metadata/dump_10_06_12_16",
"/home/mataevs/captures/metadata/dump_05_06_12_57",
"/home/mataevs/captures/metadata/dump_05_06_13_21",
"/home/mataevs/captures/metadata/dump_05_06_13_24",
"/home/mataevs/captures/metadata/dump_16_06_14_57",
"/home/mataevs/captures/metadata/dump_05_06_13_25",
"/home/mataevs/captures/metadata/dump_07_05_12_03",
"/home/mataevs/captures/metadata/dump_16_06_15_26",
"/home/mataevs/captures/metadata/dump_05_06_13_28",
"/home/mataevs/captures/metadata/dump_07_05_12_05"
]
if not os.path.exists(hog_result_dir):
os.makedirs(hog_result_dir)
if not os.path.exists(icf_result_dir):
os.makedirs(icf_result_dir)
testImages = utils.getFullImages(*filepaths)
metadata = utils.parseMetadata(*filepaths)
scales = [
[0.45, 0.5, 0.55],
[0.4, 0.45, 0.5],
[0.3, 0.35],
[0.3]
]
scaleSteps = [35, 45, 65, 90]
for sample in range(0, no_samples):
print "### Sample " + str(sample) + " ###"
imgPath = random.choice(testImages)
img = cv2.imread(imgPath)
tilt = int(metadata[imgPath]['tilt'])
if tilt > 90:
tilt = 90 - (tilt - 90)
imgScales = []
for i in range(0, len(scaleSteps)):
if tilt < scaleSteps[i]:
imgScales = scales[i]
break
print imgScales
prev_img_path = utils.get_prev_img(imgPath)
prev_img = cv2.imread(prev_img_path)
flow_rgb, boundingRect = optical_flow.optical_flow(img, prev_img)
height, width, _ = img.shape
bestWindowsHog = tester_hog.test_img(hog_classifier, imgPath, imgScales, allPositive=True, flow_rgb=flow_rgb, subwindow=boundingRect)
if bestWindowsHog != None and bestWindowsHog != []:
scale = bestWindowsHog[0][4]
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect, img_hog.shape[1], img_hog.shape[0], scale)
cv2.rectangle(img_hog, (x, y), (x+w, y+h), (0, 0, 255), thickness=2, lineType=8)
utils.draw_detections(img_hog, bestWindowsHog)
else:
scale = 0.5
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect, img_hog.shape[1], img_hog.shape[0], scale)
cv2.rectangle(img_hog, (x, y), (x+w, y+h), (0, 0, 255), thickness=2, lineType=8)
cv2.imwrite(hog_result_dir + "/sample_2_" + str(sample) + ".jpg", img_hog)
bestWindowsIcf = tester_icf.test_img(icf_classifier, imgPath, imgScales, allPositive=True, subwindow=boundingRect)
if bestWindowsIcf != None and bestWindowsIcf != []:
scale = bestWindowsIcf[0][4]
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect, img_icf.shape[1], img_icf.shape[0], scale)
cv2.rectangle(img_icf, (x, y), (x+w, y+h), (0, 0, 255), thickness=2, lineType=8)
utils.draw_detections(img_icf, bestWindowsIcf)
else:
scale = 0.5
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
if boundingRect != None:
x, y, w, h = utils.getDetectionWindow(boundingRect, img_icf.shape[1], img_icf.shape[0], scale)
cv2.rectangle(img_icf, (x, y), (x+w, y+h), (0, 0, 255), thickness=2, lineType=8)
cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg", img_icf)