def extractFromVideo(inPath,out_paths=['train.txt','test.txt','val.txt'],MIN_FRAMES = 10,DO_RESIZE=True,new_sz = (40,80)):
#Default values
MAX_FRAMES = 100
ALPHA = 0.25
print "Starting background subtraction and object tracking........... %s"%inPath
cap = cv2.VideoCapture(inPath)
ret, prev_frame = cap.read()
w = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)); h = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
w_crop = 80; h_crop = 160
def cap_read():
if cap.isOpened():
ret, frame = cap.read()
if ret:
return frame
return None
bgsubImpl = bgsub.get_instance(bgsub.BGMethod.EIGEN_SUBSTRACTION,cap_read)
bgsubImpl.setShape((h,w))
N = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT));
prv_mean = None; prev_img=None; i=0;
frames =[];
while(True):
mask_frame = bgsubImpl.process()
mask_frame = cv2.medianBlur(mask_frame,5)
mask_frame = cv2.medianBlur(mask_frame,3)
print 'Proceesing ... {0}%\r'.format((i*100/N)),
if bgsubImpl.isFinish():
break
# labelling
#img = np.zeros((h,w,3), np.uint8) # Create a black image
img = bgsubImpl.cur_frame
points = np.column_stack(np.where(mask_frame==1))
if points.shape[0] > 0:
mean = points.mean(axis=0)
if not prv_mean is None:
mean = ALPHA*mean + (1-ALPHA)*prv_mean
(y,x)=np.int32(mean);
if x-w_crop/2>0 and x+w_crop/2<w and y-h_crop/2>0 and y+h_crop/2<h :
img = img[y-h_crop/2:y+h_crop/2,x-w_crop/2:x+w_crop/2];
if DO_RESIZE:
i=i+1;
img = cv2.resize(img,new_sz);
img = np.asarray(img,dtype='float64')/256;
frames.append(img.flatten());
prv_mean = np.int32(mean);
if i % MAX_FRAMES == 0:
write(frames,out_paths,min_frames=MIN_FRAMES)
cap.release();
if frames.__len__() > MIN_FRAMES*2:
write(frames,out_paths,min_frames=MIN_FRAMES)
def tracker(inPath, outPath="out.avi"):
# Default values
MIN_FRAMES_COUNT = 1000
MIN_POINTS_TO_CLUSTER = 10
MAX_CLUSTERS = 100
SKIP_FRAMES = 0
ALPHA = 0.35
DO_CLUSTERING = False
DO_CROPPING = False
DO_LABELLING = True
DO_BOUNDING_BOX = False
random_colors = np.random.randint(256, size=(MAX_CLUSTERS, 3))
# Clustering model
# model = MeanShift(bandwidth=None, bin_seeding=True)
model = DBSCAN(eps=5, min_samples=35)
print "INPATH : ", inPath
print "OUTPATH : ", outPath
print "Starting background subtraction for object tracking..........."
cap = cv2.VideoCapture(inPath)
ret, prev_frame = cap.read()
skip_i = 0
while cap.isOpened():
skip_i = skip_i + 1
if skip_i > SKIP_FRAMES:
break
ret, prev_frame = cap.read()
if not ret:
break
w = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
w_crop = 80
h_crop = 160
fourcc = cv2.cv.CV_FOURCC(*"XVID")
if DO_CROPPING:
vidout = cv2.VideoWriter(outPath, fourcc, 20, (w_crop, h_crop))
else:
vidout = cv2.VideoWriter(outPath, fourcc, 20, (w, h))
def cap_read():
if cap.isOpened():
ret, frame = cap.read()
if ret:
return frame
return None
bgsubImpl = bgsub.get_instance(bgsub.BGMethod.EIGEN_SUBSTRACTION, cap_read)
bgsubImpl.setShape((h, w))
N = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
i = 0
N = min(N - SKIP_FRAMES, MIN_FRAMES_COUNT)
prv_mean = None
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
while True:
i = i + 1
mask_frame = bgsubImpl.process()
mask_frame = cv2.medianBlur(mask_frame, 5)
mask_frame = cv2.medianBlur(mask_frame, 3)
print "Proceesing ... {0}%\r".format((i * 100 / N)),
if bgsubImpl.isFinish() or i > MIN_FRAMES_COUNT:
break
# clustering
if DO_CLUSTERING:
points = np.where(mask_frame == 1)
points = np.column_stack(points)
points_len = points.shape[0]
if points_len > MIN_POINTS_TO_CLUSTER:
model.fit(points)
for idx, lbl in zip(range(points_len), model.labels_):
mask_frame[points[idx][0]][points[idx][1]] = lbl + 1
# labelling
# img = np.zeros((h,w,3), np.uint8) # Create a black image
img = bgsubImpl.cur_frame
# _mask_frame = mask_frame
# cv2.grabCut(img,_mask_frame,None,bgdModel,fgdModel,2,cv2.GC_INIT_WITH_MASK)
# bgmask = np.where((_mask_frame==2)|(_mask_frame==0),0,1).astype('uint8')
# img = img*bgmask[:,:,np.newaxis]
if DO_LABELLING:
for lbl, val in enumerate(np.unique(mask_frame)):
if lbl == 0:
continue
if lbl >= MAX_CLUSTERS:
break
color = tuple(random_colors[lbl])
for point_x, point_y in np.column_stack(np.where(mask_frame == val)):
cv2.circle(img, (point_y, point_x), 2, color, 1)
if DO_BOUNDING_BOX:
points = np.column_stack(np.where(mask_frame == 1))
if points.shape[0] > 0:
mean = points.mean(axis=0)
if not prv_mean is None:
mean = ALPHA * mean + (1 - ALPHA) * prv_mean
(y, x) = np.int32(mean)
if x - w_crop / 2 > 0 and x + w_crop / 2 < w and y - h_crop / 2 > 0 and y + h_crop / 2 < h:
if DO_CROPPING:
img = img[y - h_crop / 2 : y + h_crop / 2, x - w_crop / 2 : x + w_crop / 2]
else:
cv2.rectangle(
img, (x - w_crop / 2, y - h_crop / 2), (x + w_crop / 2, y + h_crop / 2), (255, 0, 0), 2
)
cv2.putText(img, "waving", (x + w_crop / 2, y + h_crop / 2), cv2.FONT_HERSHEY_SIMPLEX, 1, 190)
vidout.write(img)
prv_mean = np.int32(mean)
else:
vidout.write(img)
cap.release()
vidout.release()
cv2.destroyAllWindows()
def tracker(inPath, outPath='out.avi'):
#Default values
MIN_FRAMES_COUNT = 1000
MIN_POINTS_TO_CLUSTER = 10
MAX_CLUSTERS = 100
SKIP_FRAMES = 0
ALPHA = 0.35
DO_CLUSTERING = False
DO_CROPPING = False
DO_LABELLING = True
DO_BOUNDING_BOX = False
random_colors = np.random.randint(256, size=(MAX_CLUSTERS, 3))
#Clustering model
#model = MeanShift(bandwidth=None, bin_seeding=True)
model = DBSCAN(eps=5, min_samples=35)
print "INPATH : ", inPath
print "OUTPATH : ", outPath
print "Starting background subtraction for object tracking..........."
cap = cv2.VideoCapture(inPath)
ret, prev_frame = cap.read()
skip_i = 0
while (cap.isOpened()):
skip_i = skip_i + 1
if skip_i > SKIP_FRAMES:
break
ret, prev_frame = cap.read()
if not ret:
break
w = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
w_crop = 80
h_crop = 160
fourcc = cv2.cv.CV_FOURCC(*'XVID')
if DO_CROPPING:
vidout = cv2.VideoWriter(outPath, fourcc, 20, (w_crop, h_crop))
else:
vidout = cv2.VideoWriter(outPath, fourcc, 20, (w, h))
def cap_read():
if cap.isOpened():
ret, frame = cap.read()
if ret:
return frame
return None
bgsubImpl = bgsub.get_instance(bgsub.BGMethod.EIGEN_SUBSTRACTION, cap_read)
bgsubImpl.setShape((h, w))
N = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
i = 0
N = min(N - SKIP_FRAMES, MIN_FRAMES_COUNT)
prv_mean = None
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
while (True):
i = i + 1
mask_frame = bgsubImpl.process()
mask_frame = cv2.medianBlur(mask_frame, 5)
mask_frame = cv2.medianBlur(mask_frame, 3)
print 'Proceesing ... {0}%\r'.format((i * 100 / N)),
if bgsubImpl.isFinish() or i > MIN_FRAMES_COUNT:
break
#clustering
if DO_CLUSTERING:
points = np.where(mask_frame == 1)
points = np.column_stack(points)
points_len = points.shape[0]
if points_len > MIN_POINTS_TO_CLUSTER:
model.fit(points)
for idx, lbl in zip(range(points_len), model.labels_):
mask_frame[points[idx][0]][points[idx][1]] = lbl + 1
# labelling
#img = np.zeros((h,w,3), np.uint8) # Create a black image
img = bgsubImpl.cur_frame
#_mask_frame = mask_frame
#cv2.grabCut(img,_mask_frame,None,bgdModel,fgdModel,2,cv2.GC_INIT_WITH_MASK)
#bgmask = np.where((_mask_frame==2)|(_mask_frame==0),0,1).astype('uint8')
#img = img*bgmask[:,:,np.newaxis]
if DO_LABELLING:
for lbl, val in enumerate(np.unique(mask_frame)):
if lbl == 0:
continue
if lbl >= MAX_CLUSTERS:
break
color = tuple(random_colors[lbl])
for point_x, point_y in np.column_stack(
np.where(mask_frame == val)):
cv2.circle(img, (point_y, point_x), 2, color, 1)
if DO_BOUNDING_BOX:
points = np.column_stack(np.where(mask_frame == 1))
if points.shape[0] > 0:
mean = points.mean(axis=0)
if not prv_mean is None:
mean = ALPHA * mean + (1 - ALPHA) * prv_mean
(y, x) = np.int32(mean)
if x - w_crop / 2 > 0 and x + w_crop / 2 < w and y - h_crop / 2 > 0 and y + h_crop / 2 < h:
if DO_CROPPING:
img = img[y - h_crop / 2:y + h_crop / 2,
x - w_crop / 2:x + w_crop / 2]
else:
cv2.rectangle(img, (x - w_crop / 2, y - h_crop / 2),
(x + w_crop / 2, y + h_crop / 2),
(255, 0, 0), 2)
cv2.putText(img, "waving",
(x + w_crop / 2, y + h_crop / 2),
cv2.FONT_HERSHEY_SIMPLEX, 1, 190)
vidout.write(img)
prv_mean = np.int32(mean)
else:
vidout.write(img)
cap.release()
vidout.release()
cv2.destroyAllWindows()