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_cascade(classifier_file, icf_result_dir, checker, resultsFile):
cascade_detector = classifier.loadCascadeClassifier(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(icf_result_dir):
os.makedirs(icf_result_dir)
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]
resultsIcf = open(resultsFile + "cascade.txt", "w")
sample = 0
for imgPath in checker.getFileList():
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
#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)
flow_rgb, boundingRect = None, None
height, width, _ = img.shape
before = time.time()
pos_windows = test_img(cascade_detector,
imgPath,
imgScales,
subwindow=boundingRect)
after = time.time()
print "Sample", sample, "time elapsed=", after - before
if pos_windows != None and pos_windows != []:
utils.draw_detections(img, pos_windows)
cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg", img)
# Check detections
detections, truePositive = checker.checkDetections(
imgPath, pos_windows)
c = Counter(detections)
truePositives = c[True]
falsePositives = c[False]
falseNegative = 0 if truePositive else 1
resultsIcf.write(imgPath + " tp=" + str(truePositives) + " fp=" +
str(falsePositives) + " fn=" + str(falseNegative) +
"\n")
sample += 1
resultsIcf.close()
def test_cascade(
classifier_file,
icf_result_dir,
checker,
resultsFile):
cascade_detector = classifier.loadCascadeClassifier(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(icf_result_dir):
os.makedirs(icf_result_dir)
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]
resultsIcf = open(resultsFile + "cascade.txt", "w")
sample = 0
for imgPath in checker.getFileList():
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
#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)
flow_rgb, boundingRect = None, None
height, width, _ = img.shape
before = time.time()
pos_windows = test_img(cascade_detector, imgPath, imgScales, subwindow=boundingRect)
after = time.time()
print "Sample", sample, "time elapsed=", after-before
if pos_windows != None and pos_windows != []:
utils.draw_detections(img, pos_windows)
cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg", img)
# Check detections
detections, truePositive = checker.checkDetections(imgPath, pos_windows)
c = Counter(detections)
truePositives = c[True]
falsePositives = c[False]
falseNegative = 0 if truePositive else 1
resultsIcf.write(imgPath + " tp=" + str(truePositives) + " fp=" + str(falsePositives) + " fn=" + str(falseNegative) + "\n")
sample += 1
resultsIcf.close()
def test_multiscale_checker(hog_classifier_file, icf_classifier_file,
hog_result_dir, icf_result_dir, checker,
resultsFile):
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)
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]
resultsHog = open(resultsFile + "hog.txt", "w")
# resultsIcf = open(resultsFile + "icf.txt", "w")
sample = 0
for imgPath in checker.getFileList():
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
#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)
flow_rgb, boundingRect = None, None
height, width, _ = img.shape
posWindowsHog = tester_hog.test_img(hog_classifier,
imgPath,
imgScales,
subwindow=boundingRect)
if posWindowsHog != None and posWindowsHog != []:
utils.draw_detections(img, posWindowsHog)
cv2.imwrite(hog_result_dir + "/sample_2_" + str(sample) + ".jpg", img)
# Check detections
detections, truePositive = checker.checkDetections(
imgPath, posWindowsHog)
c = Counter(detections)
truePositives = c[True]
falsePositives = c[False]
falseNegative = 0 if truePositive else 1
resultsHog.write(imgPath + " tp=" + str(truePositives) + " fp=" +
str(falsePositives) + " fn=" + str(falseNegative) +
"\n")
# beforeIcf = time.time()
# windowsIcf = tester_icf.test_img(icf_classifier, imgPath, imgScales, allPositive=True, subwindow=boundingRect)
# afterIcf = time.time()
#
# print "Sample", sample, "time elapsed=", afterIcf-beforeIcf
#
# if windowsIcf != None and windowsIcf != []:
# scale = windowsIcf[0][4]
# img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
# utils.draw_detections(img_icf, windowsIcf)
# else:
# scale = 0.5
# img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
# cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg", img_icf)
#
# # Check detections
# detections, truePositive = checker.checkDetections(imgPath, windowsIcf)
# c = Counter(detections)
# truePositives = c[True]
# falsePositives = c[False]
# falseNegative = 0 if truePositive else 1
# resultsIcf.write(imgPath + " tp=" + str(truePositives) + " fp=" + str(falsePositives) + " fn=" + str(falseNegative) + "\n")
sample += 1
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 main(data):
## Loop through languages
trPath = os.path.join(
data["definitions"]["runtime"]["cwd"],
data["config"]["Filesystem"]["SourcePathTranslations"],
)
trPath = os.path.abspath(trPath)
languages = sorted(next(os.walk(trPath))[1])
for languageKey in languages:
data["translations"][languageKey] = {}
trLanguagePath = os.path.join(
trPath,
languageKey,
)
## Loop through artist groups
groups = sorted(next(os.walk(trLanguagePath))[1])
for groupKey in groups:
data["translations"][languageKey][groupKey] = {}
trLanguageGroupPath = os.path.join(
trLanguagePath,
groupKey,
)
## Loop through artists start begin with
artists = sorted(next(os.walk(trLanguageGroupPath))[1],
key=str.lower)
for artistKey in artists:
trLanguageGroupArtistPath = os.path.join(
trLanguageGroupPath,
artistKey,
)
data["translations"][languageKey][groupKey][artistKey] = {
"printable_name": artistKey,
"releases": [],
}
## Loop through artist’s releases
releases = sorted(next(os.walk(trLanguageGroupArtistPath))[1],
key=str.lower)
for releaseKey in releases:
trLanguageGroupArtistReleasePath = os.path.join(
trLanguageGroupArtistPath,
releaseKey,
)
data["translations"][languageKey][groupKey][artistKey][
"releases"].append({
"name": releaseKey,
"printable_name": releaseKey,
"recordings": [[]],
})
## Loop through release’s recordings
recordings = sorted(next(
os.walk(trLanguageGroupArtistReleasePath))[2],
key=str.lower)
for recordingKey in recordings:
trLanguageGroupArtistReleaseRecordingPath = os.path.join(
trLanguageGroupArtistReleasePath,
recordingKey,
)
rawContents = open(
trLanguageGroupArtistReleaseRecordingPath,
"r").read().strip()
rawText = utils.getText(rawContents)
rawMetadata = utils.getMetadata(rawContents)
data["translations"][languageKey][groupKey][artistKey][
"releases"][-1]["recordings"][0].append({
"name":
recordingKey,
"printable_name":
recordingKey,
"text":
rawText,
"metadata":
utils.parseMetadata(rawMetadata),
})
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_checker(
hog_classifier_file,
icf_classifier_file,
hog_result_dir,
icf_result_dir,
checker,
resultsFile):
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)
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]
resultsHog = open(resultsFile + "hog.txt", "w")
# resultsIcf = open(resultsFile + "icf.txt", "w")
sample = 0
for imgPath in checker.getFileList():
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
#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)
flow_rgb, boundingRect = None, None
height, width, _ = img.shape
posWindowsHog = tester_hog.test_img(hog_classifier, imgPath, imgScales, subwindow=boundingRect)
if posWindowsHog != None and posWindowsHog != []:
utils.draw_detections(img, posWindowsHog)
cv2.imwrite(hog_result_dir + "/sample_2_" + str(sample) + ".jpg", img)
# Check detections
detections, truePositive = checker.checkDetections(imgPath, posWindowsHog)
c = Counter(detections)
truePositives = c[True]
falsePositives = c[False]
falseNegative = 0 if truePositive else 1
resultsHog.write(imgPath + " tp=" + str(truePositives) + " fp=" + str(falsePositives) + " fn=" + str(falseNegative) + "\n")
# beforeIcf = time.time()
# windowsIcf = tester_icf.test_img(icf_classifier, imgPath, imgScales, allPositive=True, subwindow=boundingRect)
# afterIcf = time.time()
#
# print "Sample", sample, "time elapsed=", afterIcf-beforeIcf
#
# if windowsIcf != None and windowsIcf != []:
# scale = windowsIcf[0][4]
# img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
# utils.draw_detections(img_icf, windowsIcf)
# else:
# scale = 0.5
# img_icf = cv2.resize(img, (0, 0), fx=scale, fy=scale)
# cv2.imwrite(icf_result_dir + "/sample_2_" + str(sample) + ".jpg", img_icf)
#
# # Check detections
# detections, truePositive = checker.checkDetections(imgPath, windowsIcf)
# c = Counter(detections)
# truePositives = c[True]
# falsePositives = c[False]
# falseNegative = 0 if truePositive else 1
# resultsIcf.write(imgPath + " tp=" + str(truePositives) + " fp=" + str(falsePositives) + " fn=" + str(falseNegative) + "\n")
sample += 1
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)