def measure_occlusion(bbox,
trackedObjects,
cropSize=CROP_SIZE,
cropPad=CROP_PAD):
image = np.zeros((int(cropSize), int(cropSize)), dtype=np.bool)
fullBBox = scale_bbox(bbox, cropPad)
fullBBoxXYWH = xyxy_to_xywh(fullBBox)
fullBBoxXYWH[[2, 3]] = np.maximum(fullBBoxXYWH[[2, 3]], 1)
# Now do all objects
for obj in trackedObjects:
boxPos = obj.get_bbox()
boxPosXYWH = xyxy_to_xywh(boxPos)
if IOU(boxPos, fullBBox) < 0.001:
continue
cropCoords = np.zeros(4)
cropCoords = np.clip(boxPos - fullBBox[[0, 1, 0, 1]], 0,
fullBBoxXYWH[[2, 3, 2, 3]])
cropCoords *= cropSize * 1.0 / fullBBoxXYWH[[2, 3, 2, 3]]
cropCoords = np.clip(np.round(cropCoords), 0, cropSize).astype(int)
if (cropCoords[2] - cropCoords[0] < 1
or cropCoords[3] - cropCoords[1] < 1):
cropCoords[[0, 1]] = np.clip(cropCoords[[0, 1]] - 1, 0,
cropSize).astype(int)
cropCoords[[2, 3]] = np.clip(cropCoords[[2, 3]] + 1, 0,
cropSize).astype(int)
image[cropCoords[1]:cropCoords[3], cropCoords[0]:cropCoords[2]] = True
return np.count_nonzero(image) * 1.0 / image.size
def fix_bbox_intersection(self, bbox, gtBox, imageWidth, imageHeight):
if type(bbox) == list:
bbox = np.array(bbox)
if type(gtBox) == list:
gtBox = np.array(gtBox)
gtBoxArea = float((gtBox[3] - gtBox[1]) * (gtBox[2] - gtBox[0]))
bboxLarge = bb_util.scale_bbox(bbox, CROP_PAD)
while IOU.intersection(bboxLarge, gtBox) / gtBoxArea < AREA_CUTOFF:
bbox = bbox * .9 + gtBox * .1
bboxLarge = bb_util.scale_bbox(bbox, CROP_PAD)
return bbox
def fix_bbox_intersection(bbox, gtBox):
if type(bbox) == list:
bbox = np.array(bbox)
if type(gtBox) == list:
gtBox = np.array(gtBox)
bbox = bbox.copy()
gtBoxArea = float((gtBox[3] - gtBox[1]) * (gtBox[2] - gtBox[0]))
bboxLarge = bb_util.scale_bbox(bbox, CROP_PAD)
while IOU.intersection(bboxLarge, gtBox) / gtBoxArea < AREA_CUTOFF:
bbox = bbox * 0.9 + gtBox * 0.1
bboxLarge = bb_util.scale_bbox(bbox, CROP_PAD)
return bbox
def run_frame(im_on, gt_on):
# Necessary for linking up global variables.
global tracker
global totalIou, numFrames
global initialize
global imageNames
global ignoreFrames, initializeFrames, lostTarget
titles = []
if gt_on == 0 or not (gt[gt_on, 4] == gt[gt_on - 1, 4]
and gt[gt_on, 5] == gt[gt_on - 1, 5]
and gt[gt_on, 6] - 1 == gt[gt_on - 1, 6]):
print("beginning sequence", gt[gt_on, [5, 6]])
# Clear the state if a new sequence has started.
initialize = True
ignoreFrames = 0
initializeFrames = 0
iou = 1
robustness = 1
gtBox = gt[gt_on, :4].copy()
if DISPLAY:
inputImageBGR = cv2.imread(imageNames[im_on])
inputImage = inputImageBGR[:, :, ::-1]
imageToDraw = inputImageBGR.copy()
drawRect(imageToDraw, gtBox, PADDING * 2, [0, 255, 0])
else:
inputImage = imageNames[im_on]
if ignoreFrames > 0:
ignoreFrames -= 1
else:
if initialize:
outputBox = tracker.track("test_track", inputImage, gtBox)
initialize = False
else:
outputBox = tracker.track("test_track", inputImage)
if DISPLAY:
drawRect(imageToDraw, outputBox, PADDING, [0, 0, 255])
if initializeFrames == 0:
iou = IOU(outputBox, gtBox)
totalIou += iou
if iou == 0:
ignoreFrames = 5
initializeFrames = 10
lostTarget += 1
initialize = True
numFrames += 1
robustness = np.exp(-30.0 * lostTarget / numFrames)
else:
initializeFrames -= 1
meanIou = totalIou * 1.0 / max(numFrames, 1)
if DISPLAY:
imageToDraw[0, 0] = 255
imageToDraw[0, 1] = 0
titles.append(
"Frame %d, IOU %.2f, Mean IOU %.2f, Robustness %.2f, Dropped %d" %
(gt_on, iou, meanIou, robustness, lostTarget))
imPlots = [imageToDraw]
results = {
"gt_on": gt_on,
"meanIou": meanIou,
"robustness": robustness,
"lostTarget": lostTarget,
}
if DISPLAY:
return imPlots, titles, results
else:
return results
def is_occluded(self):
occluder_boxes = np.array([obj.get_bbox() for obj in self.occluder_boxes])
if len(occluder_boxes) == 0:
return False
return IOU.count_overlapping_boxes(occluder_boxes, self.get_bounded_bbox()) > 0
def runFrame(self, imOn, gtOn):
titles = []
if (gtOn == 0 or not (
self.gt[gtOn, 4] == self.gt[gtOn - 1, 4] and
self.gt[gtOn, 5] == self.gt[gtOn - 1, 5] and
self.gt[gtOn, 6] - 1 == self.gt[gtOn - 1, 6])):
if PRINT:
print('beginning sequence', self.gt[gtOn, [5, 6]])
# Clear the state if a new sequence has started.
self.initialize = True
self.ignoreFrames = 0
self.initializeFrames = 0
iou = 1
robustness = 1
gtBox = self.gt[gtOn, :4].copy()
if self.display:
inputImageBGR = cv2.imread(self.imageNames[imOn])
inputImage = inputImageBGR[:,:,::-1]
imageToDraw = inputImageBGR.copy()
drawRect(imageToDraw, gtBox, PADDING * 2, [0, 255, 0])
else:
inputImage = self.imageNames[imOn]
if self.ignoreFrames > 0:
self.ignoreFrames -= 1
else:
if self.initialize:
outputBox = self.tracker.track('test_track', inputImage, gtBox)
self.initialize = False
else:
outputBox = self.tracker.track('test_track', inputImage)
if self.display:
drawRect(imageToDraw, outputBox, PADDING, [0, 0, 255])
if self.initializeFrames == 0:
iou = IOU(outputBox, gtBox)
self.totalIou += iou
if iou == 0:
self.ignoreFrames = 5
self.initializeFrames = 10
self.lostTarget += 1
self.initialize = True
self.numFrames += 1
robustness = np.exp(-30.0 * self.lostTarget / self.numFrames)
else:
self.initializeFrames -= 1
meanIou = self.totalIou * 1.0 / max(self.numFrames, 1)
if self.display:
imageToDraw[0,0] = 255
imageToDraw[0,1] = 0
titles.append(
'Frame %d, IOU %.2f, Mean IOU %.2f, Robustness %.2f, Dropped %d' %
(gtOn, iou, meanIou, robustness, self.lostTarget))
imPlots = [imageToDraw]
results = {
'gtOn' : gtOn,
'meanIou' : meanIou,
'robustness' : robustness,
'lostTarget' : self.lostTarget,
}
if self.display:
return (imPlots, titles, results)
else:
return results
def render_patch(bbox,
background,
trackedObjects,
cropSize=CROP_SIZE,
cropPad=CROP_PAD):
bboxXYWH = xyxy_to_xywh(bbox)
image = np.zeros((int(cropSize), int(cropSize), 3), dtype=np.uint8)
fullBBoxXYWH = bboxXYWH.copy()
fullBBoxXYWH[[2, 3]] *= cropPad
fullBBox = xywh_to_xyxy(fullBBoxXYWH)
fullBBoxXYWH = fullBBoxXYWH
fullBBoxXYWH[[2, 3]] = np.maximum(fullBBoxXYWH[[2, 3]], 1)
# First do background
boxPos = np.array([0, 0, background.shape[1], background.shape[0]])
boxPosXYWH = xyxy_to_xywh(boxPos)
cropCoords = np.clip(boxPos - fullBBox[[0, 1, 0, 1]], 0,
fullBBoxXYWH[[2, 3, 2, 3]])
cropCoords *= (cropSize) * 1.0 / fullBBoxXYWH[[2, 3, 2, 3]]
cropCoords = np.clip(np.round(cropCoords), 0, cropSize).astype(int)
textureCrop = np.zeros(4)
textureCrop[0] = int(
max(fullBBox[0] - boxPos[0], 0) * background.shape[1] * 1.0 /
boxPosXYWH[2])
textureCrop[1] = int(
max(fullBBox[1] - boxPos[1], 0) * background.shape[0] * 1.0 /
boxPosXYWH[3])
textureCrop[2] = int(
min((fullBBox[2] - boxPos[0]) * 1.0 / boxPosXYWH[2], 1) *
background.shape[1])
textureCrop[3] = int(
min((fullBBox[3] - boxPos[1]) * 1.0 / boxPosXYWH[3], 1) *
background.shape[0])
if (textureCrop[2] - textureCrop[0] < 1
or textureCrop[3] - textureCrop[1] < 1):
textureCrop = [0, 0, 1, 1]
textureCrop = np.round(textureCrop).astype(int)
textureCrop[[0, 2]] = np.clip(textureCrop[[0, 2]], 0, background.shape[1])
textureCrop[[1, 3]] = np.clip(textureCrop[[1, 3]], 0, background.shape[0])
if cropCoords[3] > cropCoords[1] + 1 and cropCoords[2] > cropCoords[0] + 1:
image[cropCoords[1]:cropCoords[3],
cropCoords[0]:cropCoords[2], :] = (cv2.resize(
background[textureCrop[1]:textureCrop[3],
textureCrop[0]:textureCrop[2], :],
(cropCoords[2] - cropCoords[0],
cropCoords[3] - cropCoords[1])))
# Now do all objects
for obj in trackedObjects:
boxPos = obj.get_bbox()
boxPosXYWH = xyxy_to_xywh(boxPos)
if IOU(boxPos, fullBBox) < 0.001:
continue
cropCoords = np.zeros(4)
cropCoords = np.clip(boxPos - fullBBox[[0, 1, 0, 1]], 0,
fullBBoxXYWH[[2, 3, 2, 3]])
cropCoords *= cropSize * 1.0 / fullBBoxXYWH[[2, 3, 2, 3]]
cropCoords = np.clip(np.round(cropCoords), 0, cropSize).astype(int)
if (cropCoords[2] - cropCoords[0] < 1
or cropCoords[3] - cropCoords[1] < 1):
cropCoords[[0, 1]] = np.clip(cropCoords[[0, 1]] - 1, 0,
cropSize).astype(int)
cropCoords[[2, 3]] = np.clip(cropCoords[[2, 3]] + 1, 0,
cropSize).astype(int)
textureCrop = np.zeros(4, dtype=int)
textureCrop[0] = int(
max(fullBBox[0] - boxPos[0], 0) * obj.texture.shape[1] * 1.0 /
boxPosXYWH[2])
textureCrop[1] = int(
max(fullBBox[1] - boxPos[1], 0) * obj.texture.shape[0] * 1.0 /
boxPosXYWH[3])
textureCrop[2] = int(
min((fullBBox[2] - boxPos[0]) * 1.0 / boxPosXYWH[2], 1) *
obj.texture.shape[1])
textureCrop[3] = int(
min((fullBBox[3] - boxPos[1]) * 1.0 / boxPosXYWH[3], 1) *
obj.texture.shape[0])
if (textureCrop[2] - textureCrop[0] < 1
or textureCrop[3] - textureCrop[1] < 1):
textureCrop = [0, 0, 2, 2]
textureCrop = np.round(textureCrop).astype(int)
textureCrop[[0, 2]] = np.clip(textureCrop[[0, 2]], 0,
obj.texture.shape[1])
textureCrop[[1, 3]] = np.clip(textureCrop[[1, 3]], 0,
obj.texture.shape[0])
# Feathering
currentIm = image[cropCoords[1]:cropCoords[3],
cropCoords[0]:cropCoords[2], :].astype(np.float32)
newIm = cv2.resize(
obj.texture[textureCrop[1]:textureCrop[3],
textureCrop[0]:textureCrop[2], :],
(cropCoords[2] - cropCoords[0], cropCoords[3] - cropCoords[1]),
).astype(np.float32)
if (cropCoords[2] - cropCoords[0] < 1
or cropCoords[3] - cropCoords[1] < 1):
featherWeightOn = 0
else:
featherCrop = np.zeros(4)
featherCrop[0] = int(
max(fullBBox[0] - boxPos[0], 0) *
FEATHER_WEIGHT_ARRAY.shape[1] * 1.0 / boxPosXYWH[2])
featherCrop[1] = int(
max(fullBBox[1] - boxPos[1], 0) *
FEATHER_WEIGHT_ARRAY.shape[0] * 1.0 / boxPosXYWH[3])
featherCrop[2] = int(
min((fullBBox[2] - boxPos[0]) * 1.0 / boxPosXYWH[2], 1) *
FEATHER_WEIGHT_ARRAY.shape[1])
featherCrop[3] = int(
min((fullBBox[3] - boxPos[1]) * 1.0 / boxPosXYWH[3], 1) *
FEATHER_WEIGHT_ARRAY.shape[0])
if (featherCrop[2] - featherCrop[0] < 1
or featherCrop[3] - featherCrop[1] < 1):
featherCrop = [
int(CROP_SIZE / 2 - 1),
int(CROP_SIZE / 2 - 1),
int(CROP_SIZE / 2),
int(CROP_SIZE / 2)
]
featherCrop = np.round(featherCrop).astype(int)
featherCrop[[0, 2]] = np.clip(featherCrop[[0, 2]], 0,
FEATHER_WEIGHT_ARRAY.shape[1])
featherCrop[[1, 3]] = np.clip(featherCrop[[1, 3]], 0,
FEATHER_WEIGHT_ARRAY.shape[0])
featherWeightOn = cv2.resize(
FEATHER_WEIGHT_ARRAY[featherCrop[1]:featherCrop[3],
featherCrop[0]:featherCrop[2], :],
(cropCoords[2] - cropCoords[0],
cropCoords[3] - cropCoords[1])).astype(np.float32) / 255.0
image[cropCoords[1]:cropCoords[3], cropCoords[0]:cropCoords[2], :] = (
(newIm * featherWeightOn + currentIm *
(1 - featherWeightOn)).astype(np.uint8))
return image