def test():
filepaths = [
"/home/mataevs/captures/metadata/dump_05_05_01_50",
"/home/mataevs/captures/metadata/dump_05_06_13_10",
# "/home/mataevs/captures/metadata/dump_07_05_11_07",
"/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_07_05_12_02",
"/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"
]
testImages = utils.getFullImages(*filepaths)
with open("annotations.txt", "a") as annotationFile:
while True:
imgPath = random.choice(testImages)
testImages.remove(imgPath)
annotateImage(imgPath, annotationFile)
def test():
filepaths = [
"/home/mataevs/captures/metadata/dump_05_05_01_50",
"/home/mataevs/captures/metadata/dump_05_06_13_10",
# "/home/mataevs/captures/metadata/dump_07_05_11_07",
"/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_07_05_12_02",
"/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"
]
testImages = utils.getFullImages(*filepaths)
with open("annotations.txt", "a") as annotationFile:
while True:
imgPath = random.choice(testImages)
testImages.remove(imgPath)
annotateImage(imgPath, annotationFile)
def test(input_classifier_name, scale=0.6):
c = load_classifier(input_classifier_name)
# testImages = utils.getFullImages(
# "/home/mataevs/ptz/dumpNew1",
# "/home/mataevs/ptz/dumpNew2",
# "/home/mataevs/ptz/dumpNew3",
# "/home/mataevs/ptz/ns1",
# "/home/mataevs/ptz/ns2",
# "/home/mataevs/ptz/ns3",
# "/home/mataevs/ptz/ns4")
testImages = utils.getFullImages(
"/home/mataevs/ptz/INRIAPerson/Test/pos"
)
while True:
imgPath = random.choice(testImages)
bestWindow = test_img(c, imgPath, scale)
img = cv2.imread(imgPath)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img, [bestWindow])
cv2.imshow("detection", img)
key = cv2.waitKey(0)
if key == 27:
exit(1)
def test(input_classifier_name, scale=0.6):
c = load_classifier(input_classifier_name)
# testImages = utils.getFullImages(
# "/home/mataevs/ptz/dumpNew1",
# "/home/mataevs/ptz/dumpNew2",
# "/home/mataevs/ptz/dumpNew3",
# "/home/mataevs/ptz/ns1",
# "/home/mataevs/ptz/ns2",
# "/home/mataevs/ptz/ns3",
# "/home/mataevs/ptz/ns4")
testImages = utils.getFullImages("/home/mataevs/ptz/INRIAPerson/Test/pos")
while True:
imgPath = random.choice(testImages)
bestWindow = test_img(c, imgPath, scale)
img = cv2.imread(imgPath)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img, [bestWindow])
cv2.imshow("detection", img)
key = cv2.waitKey(0)
if key == 27:
exit(1)
def train(classifier_out_name, noInitialFeatures, noWantedFeatures, noEstimators):
posImages = utils.getFullImages(
"/home/mataevs/ptz/INRIAPerson/train/pos",
"/home/mataevs/ptz/positive")
posImages = utils.randomize(posImages)
negImages = utils.getFullImages(
"/home/mataevs/ptz/INRIAPerson/train/neg",
"/home/mataevs/ptz/negative")
negImages = utils.randomize(negImages)
print len(posImages)
print len(negImages)
c = Classifier()
print "Starting training"
c.train(posImages, negImages, initialFeatures=noInitialFeatures, wantedFeatures=noWantedFeatures, noEstimators=noEstimators)
print "Finished training"
c.saveClassifier(classifier_out_name)
def train(classifier_out_name, noInitialFeatures, noWantedFeatures,
noEstimators):
posImages = utils.getFullImages("/home/mataevs/ptz/INRIAPerson/train/pos",
"/home/mataevs/ptz/positive")
posImages = utils.randomize(posImages)
negImages = utils.getFullImages("/home/mataevs/ptz/INRIAPerson/train/neg",
"/home/mataevs/ptz/negative")
negImages = utils.randomize(negImages)
print len(posImages)
print len(negImages)
c = Classifier()
print "Starting training"
c.train(posImages,
negImages,
initialFeatures=noInitialFeatures,
wantedFeatures=noWantedFeatures,
noEstimators=noEstimators)
print "Finished training"
c.saveClassifier(classifier_out_name)
from classifier import *
filepaths = [
"/home/mataevs/captures/simple/set1",
"/home/mataevs/captures/simple/set2",
"/home/mataevs/captures/simple/set3",
"/home/mataevs/captures/simple/set4",
"/home/mataevs/captures/simple/ns1",
"/home/mataevs/captures/simple/ns2",
"/home/mataevs/captures/simple/ns3",
"/home/mataevs/captures/simple/ns4",
"/home/mataevs/captures/simple/dumpNew1",
"/home/mataevs/captures/simple/dumpNew2",
"/home/mataevs/captures/simple/dumpNew3",
]
images = utils.getFullImages(*filepaths)
random.shuffle(images)
c = loadCascadeClassifier("cascade_classifier_100_500_2k.dump")
resultsFile = open("hog_corpus_neg.txt", "w")
negatives = 0
imgIndex = 0
while negatives < 10000:
imgPath = images[imgIndex]
for scale in [0.3, 0.4, 0.5]:
windows = c.getWindowsAndDescriptors(imgPath, scale)
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)
from classifier import *
filepaths = [
"/home/mataevs/captures/simple/set1",
"/home/mataevs/captures/simple/set2",
"/home/mataevs/captures/simple/set3",
"/home/mataevs/captures/simple/set4",
"/home/mataevs/captures/simple/ns1",
"/home/mataevs/captures/simple/ns2",
"/home/mataevs/captures/simple/ns3",
"/home/mataevs/captures/simple/ns4",
"/home/mataevs/captures/simple/dumpNew1",
"/home/mataevs/captures/simple/dumpNew2",
"/home/mataevs/captures/simple/dumpNew3",
]
images = utils.getFullImages(*filepaths)
random.shuffle(images)
c = loadCascadeClassifier("cascade_classifier_100_500_2k.dump")
resultsFile = open("hog_corpus_neg.txt", "w")
negatives = 0
imgIndex = 0
while negatives < 10000:
imgPath = images[imgIndex]
for scale in [0.3, 0.4, 0.5]:
windows = c.getWindowsAndDescriptors(imgPath, scale)
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, imgpaths,
kinect_detections, hog_result_dir, icf_result_dir):
hog_classifier = tester_hog.load_classifier(hog_classifier_file)
icf_classifier = tester_icf.load_classifier(icf_classifier_file)
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(*imgpaths)
metadata = utils.parseMetadata(*imgpaths)
kinect_ts = read_kinect_metadata(kinect_detections)
print metadata
print kinect_ts
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 kinectts in kinect_ts:
sel = min(metadata.items(),
key=lambda mt: abs(kinectts - mt[1]['time']))
print str(kinectts) + " " + str(sel[1]['time'])
imgPath = sel[0]
imgTs = sel[1]['time']
print "### Sample " + str(imgPath) + " ###"
img = cv2.imread(imgPath)
tilt = int(metadata[imgPath]['tilt'])
print tilt
imgScales = []
for i in range(0, len(scaleSteps)):
if tilt < scaleSteps[i]:
imgScales = scales[i]
break
print imgScales
bestWindowHog = tester_hog.test_img(hog_classifier, imgPath, imgScales)
if bestWindowHog != None:
scale = bestWindowHog[4]
print "best scale hog = " + str(scale)
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img_hog, [bestWindowHog[0:4]])
else:
scale = 0.5
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
cv2.imwrite(hog_result_dir + "/" + str(imgTs) + ".jpg", img_hog)
bestWindowIcf = tester_icf.test_img(icf_classifier, imgPath, imgScales)
if bestWindowIcf != None:
scale = bestWindowIcf[4]
print "best scale icf = " + str(scale)
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img_icf, [bestWindowIcf[0:4]])
cv2.imshow("icf", img_icf)
else:
scale = 0.5
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
cv2.imshow("icf", img_icf)
cv2.imwrite(icf_result_dir + "/" + str(imgTs) + ".jpg", img_icf)
def test_multiscale(
hog_classifier_file,
icf_classifier_file,
imgpaths,
kinect_detections,
hog_result_dir,
icf_result_dir):
hog_classifier = tester_hog.load_classifier(hog_classifier_file)
icf_classifier = tester_icf.load_classifier(icf_classifier_file)
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(*imgpaths)
metadata = utils.parseMetadata(*imgpaths)
kinect_ts = read_kinect_metadata(kinect_detections)
print metadata
print kinect_ts
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 kinectts in kinect_ts:
sel = min(metadata.items(), key=lambda mt: abs(kinectts - mt[1]['time']))
print str(kinectts) + " " + str(sel[1]['time'])
imgPath = sel[0]
imgTs = sel[1]['time']
print "### Sample " + str(imgPath) + " ###"
img = cv2.imread(imgPath)
tilt = int(metadata[imgPath]['tilt'])
print tilt
imgScales = []
for i in range(0, len(scaleSteps)):
if tilt < scaleSteps[i]:
imgScales = scales[i]
break
print imgScales
bestWindowHog = tester_hog.test_img(hog_classifier, imgPath, imgScales)
if bestWindowHog != None:
scale = bestWindowHog[4]
print "best scale hog = " + str(scale)
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img_hog, [bestWindowHog[0:4]])
else:
scale = 0.5
img_hog = cv2.resize(img, (0, 0), fx=scale, fy=scale)
cv2.imwrite(hog_result_dir + "/" + str(imgTs) + ".jpg", img_hog)
bestWindowIcf = tester_icf.test_img(icf_classifier, imgPath, imgScales)
if bestWindowIcf != None:
scale = bestWindowIcf[4]
print "best scale icf = " + str(scale)
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
utils.draw_detections(img_icf, [bestWindowIcf[0:4]])
cv2.imshow("icf", img_icf)
else:
scale = 0.5
img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
cv2.imshow("icf", img_icf)
cv2.imwrite(icf_result_dir + "/" + str(imgTs) + ".jpg", img_icf)