def add_routine(originalFilePath, routineFullPath):
if not os.path.exists(routineFullPath):
print("File not found:", routineFullPath)
return
# Open database
db = getDb()
routineRelPath = routineFullPath.replace(consts.videosRootPath, '')
originalRelPath = originalFilePath.replace(consts.videosRootPath, '')
competition = routineFullPath.split(os.sep)[-3]
# Use OpenCV to get video meta data
cap = helper_funcs.open_video(routineFullPath)
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
try:
routine = Routine(routineRelPath, originalRelPath, competition, height,
width, fps, frame_count)
db.add(routine)
db.commit()
print("Routine addded:", routineFullPath)
output_images.generate_thumbnail(routine)
except sqlite3.IntegrityError:
print("Routine already in db:", routineFullPath)
def generate_thumbnail(routine):
thumbFilePath = os.path.join(consts.thumbDir, '{}.jpg'.format(routine.id))
# if os.path.exists(thumbFilePath):
# print('Found existing thumb')
# return
cap = helper_funcs.open_video(routine.path)
_ret, frame = cap.read()
# Calculate dimens
percentToKeep = 0.35
percentFromCenter = percentToKeep / 2
height, width = frame.shape[:2]
cropTop = int(height * percentFromCenter)
cropBottom = int(height * (1 - percentFromCenter))
cropLeft = int(width * percentFromCenter)
cropRight = int(width * (1 - percentFromCenter))
# Crop
frame = frame[cropTop:cropBottom, cropLeft:cropRight]
# Shrink
frame = cv2.resize(frame, (frame.shape[1] / 2, frame.shape[0] / 2))
cv2.imwrite(thumbFilePath, frame)
print('Thumbnail created: {}'.format(thumbFilePath))
def assign_sex_designations(db):
import cv2
for routine in db.query(Routine).filter(Routine.sex == None):
cap = helper_funcs.open_video(routine.path)
while 1:
_ret, frame = cap.read()
cv2.imshow('Visualise', frame)
k = cv2.waitKey(10) & 0xff
if k == ord('m'):
routine.sex = 'male'
db.commit()
break
elif k == ord('f'):
routine.sex = 'female'
db.commit()
break
elif k == ord('\n') or k == ord('\r'): # return/enter key
break
elif k == ord('q') or k == 27: # q/ESC
print("Exiting...")
exit()
if cap.get(cv2.CAP_PROP_POS_FRAMES) == cap.get(
cv2.CAP_PROP_FRAME_COUNT):
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
def bounce_to_gif(db, bounce):
from helpers.consts import bouncesRootPath
gifFilepath = bouncesRootPath + '{}.gif'.format(bounce.id)
if os.path.exists(gifFilepath):
print("Image exists: {}".format(gifFilepath))
return
import imageio
routine = bounce.routine
cap = helper_funcs.open_video(routine.path)
cap.set(cv2.CAP_PROP_POS_FRAMES, bounce.start_frame)
peak = bounce.apex_frame
peaksIndex = 0
images = []
while 1:
_ret, frame = cap.read()
if int(cap.get(cv2.CAP_PROP_POS_FRAMES)) >= bounce.end_frame:
break
if peak == int(cap.get(cv2.CAP_PROP_POS_FRAMES)):
peaksIndex = len(images)
try:
frame_data = db.query(Frame).filter_by(routine_id=routine.id, frame_num=cap.get(cv2.CAP_PROP_POS_FRAMES)).one()
except NoResultFound:
continue
cx = frame_data.center_pt_x
cy = frame_data.center_pt_y
x1, x2, y1, y2 = helper_funcs.crop_points_constrained(routine.video_height, routine.video_width, cx, cy, routine.crop_length)
if not frame_data.pose:
continue
pose = np.array(json.loads(frame_data.pose))
frameCropped = frame[y1:y2, x1:x2]
frameCropped = cv2.resize(frameCropped, (256, 256))
# cv2.putText(frameCropped, '{}'.format(prevBounceName), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
cv2.putText(frameCropped, '{}'.format(frame_data.frame_num), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
frameCropped = _draw_pose_on_frame(pose, frameCropped)
images.append(cv2.cvtColor(frameCropped, cv2.COLOR_BGR2RGB))
print("Writing to: {}".format(gifFilepath))
imageio.mimwrite(gifFilepath, images[::2])
if peaksIndex == 0:
peaksIndex = int(len(images) / 2)
jpgFilepath = bouncesRootPath + '{}.jpg'.format(bounce.id)
imageio.imwrite(jpgFilepath, images[peaksIndex])
def skill_into_filmstrip(bounce):
cap = helper_funcs.open_video(bounce.routine.path)
capWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
capHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
frameClipping = 8
numFrames = 6
start = bounce.start_frame + 6
end = bounce.end_frame - 0
step = (end - start) / numFrames
step = int(round(step, 0))
framesToSave = np.arange(start + (step / 2),
end - (step / 2),
step,
dtype=int)
whitespace = 4
width = 255
leftCrop = int((capWidth * 0.5) - width / 2)
rightCrop = int(leftCrop + width)
filmStrip = np.ones(shape=(capHeight * 0.8, (width * len(framesToSave)) +
(whitespace * len(framesToSave) - 1), 3),
dtype=np.uint8)
filmStrip[:] = 250
for i, frameNum in enumerate(framesToSave):
cap.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
_ret, frame = cap.read()
# possible improvement
trackPerson = frame[0:int(capHeight * 0.8), leftCrop:rightCrop]
start = ((whitespace + width) * i)
filmStrip[0:int(capHeight * 0.8), start:start + width] = trackPerson
cv2.imshow('Filmstrip', filmStrip)
cv2.waitKey(50)
# imgName = "C:/Users/psdco/Videos/{}/{}.png".format(bounce.routine.getAsDirPath(), bounce.skill_name)
imgName = consts.thesisImgPath + "{}_strip.png".format(bounce.skill_name)
print("Writing frame to {}".format(imgName))
ret = cv2.imwrite(imgName, filmStrip)
if not ret:
print("Couldn't write image {}\nAbort!".format(imgName))
# Add frame count to database table
routines = db.query(Routine).all()
for routine in routines:
pathToVideo = os.path.join(consts.videosRootPath, routine.path)
cap = cv2.VideoCapture(pathToVideo)
routine.frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
db.commit()
exit()
routines = db.query(Routine).filter(Routine.use == 1).all()
# routines = db.query(Routine).filter(Routine.id == 35).all()
# if False:
# Delete frames coming from some other routine... :/
for routine in routines:
cap = helper_funcs.open_video(routine.path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if routine.frames[-1].frame_num > frame_count:
for frame in routine.frames:
if frame.frame_num > frame_count:
db.delete(frame)
db.commit()
# else:
# Delete duplicate entries for routine in frame_data (Won't catch back to back)
for routine in routines:
frameDataIds = [frame.id for frame in routine.frames]
diff = np.diff(frameDataIds)
boundaryIndex = np.nonzero(diff > 1)[0]
if boundaryIndex:
def find_trampoline(routine):
cap = helper_funcs.open_video(routine.path)
# print("Trampoline Top has not yet been set!")
print(
"Use the awsd keys to adjust the cross hairs. Use qe keys to adjust width"
)
print("Press ENTER to finish, and ESC/'q' to quit")
_ret, frame = cap.read()
# Take a best guess at where it might be
trampoline = {
'top':
routine.trampoline_top
if routine.trampoline_top else _trampoline_top_best_guess(frame),
'center':
routine.trampoline_center
if routine.trampoline_center else routine.video_width /
2, # TODO this is a reasonable default, a poor solution
'width':
routine.trampoline_width if routine.trampoline_width else 236,
}
trampoline['ends'] = calcTrampolineEnds(trampoline['width'])
use = routine.use
while 1: # video will loop back to the start
_ret, frame = cap.read()
maskLeftBorder = int(trampoline['center'] - (trampoline['ends'] / 2))
maskRightBorder = int(trampoline['center'] + (trampoline['ends'] / 2))
maskAroundTrampoline = np.zeros(shape=(routine.video_height,
routine.video_width),
dtype=np.uint8)
maskAroundTrampoline[
0:trampoline['top'],
maskLeftBorder:maskRightBorder] = 255 # [y1:y2, x1:x2]
frameCropped = cv2.bitwise_and(frame, frame, mask=maskAroundTrampoline)
# (x1 y1), (x2, y2)
cv2.line(frame, (0, trampoline['top']),
(routine.video_width, trampoline['top']), (0, 255, 0), 1)
# Vertical Lines
cv2.line(frame, (trampoline['center'], 0),
(trampoline['center'], routine.video_height), (0, 255, 0), 1)
cv2.line(frame, (trampoline['center'] + (trampoline['width'] / 2), 0),
(trampoline['center'] +
(trampoline['width'] / 2), routine.video_height),
(0, 0, 255), 1)
cv2.line(frame, (trampoline['center'] - (trampoline['width'] / 2), 0),
(trampoline['center'] -
(trampoline['width'] / 2), routine.video_height),
(0, 0, 255), 1)
cv2.line(frame, (trampoline['center'] + (trampoline['ends'] / 2), 0),
(trampoline['center'] +
(trampoline['ends'] / 2), routine.video_height), (0, 255, 0),
1)
cv2.line(frame, (trampoline['center'] - (trampoline['ends'] / 2), 0),
(trampoline['center'] -
(trampoline['ends'] / 2), routine.video_height), (0, 255, 0),
1)
cv2.imshow('Frame', frame)
cv2.imshow('Frame Cropped', frameCropped)
k = cv2.waitKey(10)
# TODO this is broken... DONE: Fixed it with asdw
# print 'You pressed %d (0x%x), LSB: %d (%s)' % (k, k, k % 256,repr(chr(k % 256)) if k % 256 < 128 else '?')
if k == ord('u'): # 2490368: # up
use = 1 if use == 0 else 0
print("Use ", use)
elif k == ord('w'): # 2490368: # up
trampoline['top'] -= 1
elif k == ord('s'): # 2621440: # down
trampoline['top'] += 1
elif k == ord('a'): # 2424832: # left
trampoline['center'] -= 1
elif k == ord('d'): # 2555904: # right
trampoline['center'] += 1
elif k == ord('e'): # widen the width
trampoline['width'] += 2
trampoline['ends'] = calcTrampolineEnds(trampoline['width'])
elif k == ord('q'): # narrow the width
trampoline['width'] -= 2
trampoline['ends'] = calcTrampolineEnds(trampoline['width'])
elif k == ord('\n') or k == ord('\r'): # return/enter key
cv2.imwrite(consts.thesisImgPath + "trampoline_detect_2.png",
frame)
break
elif k == ord('q') or k == 27: # q/ESC
print("Exiting...")
exit()
elif k == ord('p'): # p (print). Saves image for the report
cv2.imwrite(consts.thesisImgPath + "trampoline_detect_2.png",
frame)
# Loop until return or exit pressed
if cap.get(cv2.CAP_PROP_POS_FRAMES) == cap.get(
cv2.CAP_PROP_FRAME_COUNT):
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
cv2.destroyAllWindows()
cap.release()
return trampoline
def save_cropped_frames(db, routine, frames, suffix=None):
routineDirPath = routine.getAsDirPath(suffix, create=True)
# plt.figure()
position = np.array([routine.video_height - frame_data.center_pt_y for frame_data in frames])
scaleWithPerson = False
# scaleWithPerson = frames[0].hull_max_length is not None
cropLengths = []
cropLength = 0
if scaleWithPerson: # hull length
# Compensate... something
cropLengths = np.array([frame_data.hull_max_length for frame_data in frames])
cropLengths[np.nonzero(position < np.median(position))] = int(np.average(cropLengths) * 1.1)
# plt.plot(cropLengths, label="Hull Length", color="blue")
# # plt.axhline(np.average(cropLengths), label="Average Length", color="blue")
# # plt.axhline(np.median(cropLengths), label="Med Length", color="purple")
else: # Ellipse lengths
hullLengths = [frame_data.hull_max_length for frame_data in frames]
# plt.plot(hullLengths, label="Hull Length", color="blue")
cropLength = helper_funcs.get_crop_length(hullLengths)
routine.crop_length = cropLength
db.commit()
# # plt.axhline(np.average(hullLengths), label="Average Length", color="blue")
# plt.axhline(cropLength, label="Percentile", color="blue")
# plt.axhline(routine.crop_length, label="routine.crop_length", color="orange")
# plt.plot(position, label="Position", color="green")
# plt.xlabel("Time (s)")
# plt.legend(loc="best")
# plt.show(block=False)
trampolineTouches = np.array([frame.trampoline_touch for frame in routine.frames])
trampolineTouches = helper_funcs.trim_touches_by_2(trampolineTouches)
personMasks = helper_funcs.load_zipped_pickle(routine.personMasksPath())
# Create videos
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'DIB ')
frameCroppedVid = cv2.VideoWriter(consts.videosRootPath + 'savedFrames.avi', fourcc, 30.0, (256, 256))
cap = helper_funcs.open_video(routine.path)
frame = []
for i, frame_data in enumerate(frames):
# ignore frames where trampoline is touched
if trampolineTouches[i] == 1:
continue
# ignore any frame that aren't tracked
while frame_data.frame_num != cap.get(cv2.CAP_PROP_POS_FRAMES):
ret, frame = cap.read()
if not ret:
print('Something went wrong')
return
cx = frame_data.center_pt_x
cy = frame_data.center_pt_y
# Use original background or darken
if True:
frameCropped = track.highlightPerson(frame, np.array(json.loads(personMasks[frame_data.frame_num]), dtype=np.uint8), cx, cy, cropLength)
else:
x1, x2, y1, y2 = helper_funcs.crop_points_constrained(routine.video_height, routine.video_width, cx, cy, cropLength)
frameCropped = frame[y1:y2, x1:x2]
frameCropped = cv2.resize(frameCropped, (256, 256))
frameCroppedVid.write(frameCropped)
# cv2.imshow('Track ', frameCropped)
# k = cv2.waitKey(50) & 0xff
imgName = routineDirPath + "frame_{0:04}.png".format(frame_data.frame_num)
# print("Writing frame to {}".format(imgName))
# cv2.imwrite(imgName, frameCropped)
# Done
cap.release()
print("Done saving frames")
def play_frames(db,
routine,
start_frame=1,
end_frame=-1,
show_pose=True,
show_full=False):
# temp
saveReportImages = False
waitTime = 40
goOneFrame = False
paused = False
prevBounceName = ''
cap = helper_funcs.open_video(routine.path)
cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
if end_frame == -1:
end_frame = cap.get(cv2.CAP_PROP_FRAME_COUNT)
# f, axarr = plt.subplots(12, sharex=True)
# f.canvas.set_window_title(routine.prettyName())
fourcc = cv2.VideoWriter_fourcc(*'DIB ')
frameCroppedVid = cv2.VideoWriter(
consts.videosRootPath + 'posedCropped.avi', fourcc, 30.0, (256, 256))
while True:
if goOneFrame or not paused:
_ret, frame = cap.read()
# Loop forever
if cap.get(cv2.CAP_PROP_POS_FRAMES) >= end_frame:
break
cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
try:
frame_data = db.query(Frame).filter_by(
routine_id=routine.id,
frame_num=cap.get(cv2.CAP_PROP_POS_FRAMES)).one()
except NoResultFound:
continue
thisBounce = frame_data.bounce
if thisBounce and prevBounceName != thisBounce.skill_name:
# plot_frame_angles(thisBounce.skill_name, thisBounce.getFrames(db), axarr)
prevBounceName = thisBounce.skill_name
cx = frame_data.center_pt_x
cy = frame_data.center_pt_y
x1, x2, y1, y2 = helper_funcs.crop_points_constrained(
routine.video_height, routine.video_width, cx, cy,
routine.crop_length)
if frame_data.pose is not None:
pose = np.array(json.loads(frame_data.pose))
# Show full frame
if show_full:
for p_idx in range(14):
pose_x = int((cx - routine.padding) + pose[0, p_idx])
pose_y = int((cy - routine.padding) + pose[1, p_idx])
color = consts.poseColors[
calc_angles.pose_aliai['hourglass'][p_idx]][1]
cv2.circle(frame, (pose_x, pose_y),
5,
color,
thickness=cv2.FILLED)
cv2.putText(frame, '{}'.format(frame_data.frame_num),
(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 255, 255))
cv2.imshow('HG Smooth', frame)
# Show cropped
else:
frameCropped = frame[y1:y2, x1:x2]
frameCropped = cv2.resize(frameCropped, (256, 256))
frameCropped = _draw_pose_on_frame(pose, frameCropped)
if saveReportImages: # p (print). Saves image for the report
cv2.imwrite(consts.thesisImgPath + "viz_pose.png",
frameCropped)
print("wait")
# cv2.putText(frameCropped, '{}'.format(prevBounceName), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
# cv2.putText(frameCropped, '{}'.format(frame_data.frame_num), (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
frameCroppedVid.write(frameCropped)
cv2.imshow(routine.prettyName(), frameCropped)
else:
# Ignore frames that haven't got pose info
if show_pose:
continue
cv2.circle(frame, (cx, cy), 3, (0, 0, 255), cv2.FILLED)
if show_full:
cv2.imshow(routine.prettyName(), frame)
else:
frameCropped = frame[y1:y2, x1:x2]
cv2.imshow(routine.prettyName(), frameCropped)
if goOneFrame:
goOneFrame = False
k = cv2.waitKey(waitTime) & 0xff
if k == ord('k'): # play pause
paused = not paused
elif k == ord('j'): # prev frame
goOneFrame = True
cap.set(cv2.CAP_PROP_POS_FRAMES,
cap.get(cv2.CAP_PROP_POS_FRAMES) - 2)
elif k == ord('l'): # next frame
goOneFrame = True
elif k == ord('.'): # speed up
waitTime -= 5
print(waitTime)
elif k == ord(','): # slow down
waitTime += 5
print(waitTime)
elif k >= ord('0') and k <= ord('9'):
num = k - ord('0')
frameToJumpTo = (cap.get(cv2.CAP_PROP_FRAME_COUNT) / 10) * num
cap.set(cv2.CAP_PROP_POS_FRAMES, frameToJumpTo)
goOneFrame = True
elif k == ord('\n') or k == ord('\r'): # return/enter key
cv2.destroyAllWindows()
break
elif k == ord('q') or k == 27: # q/ESC
print("Exiting...")
exit()
def play_frames_of_2(db,
routine1,
routine2,
start_frame1=1,
end_frame1=-1,
start_frame2=1,
end_frame2=-1,
show_full=False):
waitTime = 80
playOneFrame = False
paused = False
cap1 = helper_funcs.open_video(routine1.path)
if end_frame1 == -1:
end_frame1 = cap1.get(cv2.CAP_PROP_FRAME_COUNT)
cap2 = helper_funcs.open_video(routine2.path)
if end_frame2 == -1:
end_frame2 = cap2.get(cv2.CAP_PROP_FRAME_COUNT)
show_pose = True
bothFrames = np.zeros(shape=(256, 256 * 2, 3), dtype=np.uint8)
# Create a list of frames from each to be played
frame_datas1 = db.query(Frame).filter(Frame.routine_id == routine1.id,
Frame.frame_num >= start_frame1,
Frame.frame_num < end_frame1,
Frame.pose != None).all()
frame_datas2 = db.query(Frame).filter(Frame.routine_id == routine2.id,
Frame.frame_num >= start_frame2,
Frame.frame_num < end_frame2,
Frame.pose != None).all()
frame_nums1 = [frame_data.frame_num for frame_data in frame_datas1]
frame_nums2 = [frame_data.frame_num for frame_data in frame_datas2]
num_frames1 = len(frame_datas1)
num_frames2 = len(frame_datas2)
frame_data1_ptr = 0
frame_data2_ptr = 0
cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_nums1[0])
cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_nums2[0])
_ret, frame1 = cap1.read()
_ret, frame2 = cap2.read()
while True:
if playOneFrame or not paused:
frame_data_ptr_video = [
cap1.get(cv2.CAP_PROP_POS_FRAMES),
cap2.get(cv2.CAP_PROP_POS_FRAMES)
]
frame_data1 = frame_datas1[frame_data1_ptr]
frame_data2 = frame_datas2[frame_data2_ptr]
cx1 = frame_data1.center_pt_x
cy1 = frame_data1.center_pt_y
cx2 = frame_data2.center_pt_x
cy2 = frame_data2.center_pt_y
if show_pose:
# if pose is None, skip showing this frame
try:
pose1 = np.array(json.loads(frame_data1.pose))
pose2 = np.array(json.loads(frame_data2.pose_unfiltered))
except TypeError:
continue
# Show full frame
if show_full:
pass
# Show cropped
else:
x1, x2, y1, y2 = helper_funcs.crop_points_constrained(
routine1.video_height, routine1.video_width, cx1, cy1,
routine1.crop_length)
frameCropped1 = frame1[y1:y2, x1:x2]
frameCropped1 = cv2.resize(frameCropped1, (256, 256))
x1, x2, y1, y2 = helper_funcs.crop_points_constrained(
routine2.video_height, routine2.video_width, cx2, cy2,
routine2.crop_length)
frameCropped2 = frame2[y1:y2, x1:x2]
frameCropped2 = cv2.resize(frameCropped2, (256, 256))
frameCropped1 = _draw_pose_on_frame(pose1, frameCropped1)
bothFrames[0:256, 0:256] = frameCropped1
cv2.putText(bothFrames,
'{}'.format(frame_nums1[frame_data1_ptr]),
(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 255, 255))
frameCropped2 = _draw_pose_on_frame(pose2, frameCropped2)
bothFrames[0:256, 256:512] = frameCropped2
cv2.putText(bothFrames,
'{}'.format(frame_nums2[frame_data2_ptr]),
(266, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 255, 255))
cv2.putText(
bothFrames,
'{}'.format(max(frame_data1_ptr,
frame_data2_ptr)), (10, 35),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
cv2.imshow('Pose', bothFrames)
frame_data1_ptr += 1
frame_data2_ptr += 1
if playOneFrame:
playOneFrame = False
k = cv2.waitKey(waitTime) & 0xff
if k == ord('k'): # play pause
paused = not paused
elif k == ord('j'): # prev frame
playOneFrame = True
frame_data1_ptr = helper_funcs.clip_wrap(frame_data1_ptr - 2, 0,
num_frames1 - 1)
frame_data2_ptr = helper_funcs.clip_wrap(frame_data2_ptr - 2, 0,
num_frames2 - 1)
cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_nums1[frame_data1_ptr])
cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_nums2[frame_data2_ptr])
elif k == ord('l'): # next frame
playOneFrame = True
elif k == ord('.'): # speed up
waitTime -= 5
print(waitTime)
elif k == ord(','): # slow down
waitTime += 5
print(waitTime)
elif k == ord('\n') or k == ord('\r'): # return/enter key
break
elif k == ord('q') or k == 27: # q/ESC
print("Exiting...")
exit()
# Loop forever, allowing shorter sequence to pause for longer sequence
if frame_data1_ptr >= num_frames1 and frame_data2_ptr >= num_frames2:
frame_data1_ptr = 0
frame_data2_ptr = 0
cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_nums1[frame_data1_ptr])
cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_nums2[frame_data2_ptr])
elif frame_data1_ptr >= num_frames1:
frame_data1_ptr -= 1
elif frame_data2_ptr >= num_frames2:
frame_data2_ptr -= 1
# Let video capture catch up
if frame_data1_ptr <= num_frames1:
while True:
desiredFNum = frame_nums1[frame_data1_ptr]
vidFNum = cap1.get(cv2.CAP_PROP_POS_FRAMES)
if vidFNum < desiredFNum:
_ret, frame1 = cap1.read()
elif vidFNum == desiredFNum:
break
elif vidFNum > desiredFNum: # if the video is further ahead than we want, force it back. This is slow...
cap1.set(cv2.CAP_PROP_POS_FRAMES, desiredFNum)
if frame_data2_ptr <= num_frames2:
while True:
desiredFNum = frame_nums2[frame_data2_ptr]
vidFNum = cap2.get(cv2.CAP_PROP_POS_FRAMES)
if vidFNum < desiredFNum:
_ret, frame2 = cap2.read()
elif vidFNum == desiredFNum:
break
elif vidFNum > desiredFNum: # if the video is further ahead than we want, force it back. This is slow...
cap2.set(cv2.CAP_PROP_POS_FRAMES, desiredFNum)
def track_gymnast(db, routine):
# temp for report
global saveReportImages
global joinNearbyContours
centerPoints = {}
hullLengths = {}
trampolineTouches = {}
personMasks = {}
print("Starting to track gymnast")
cap = helper_funcs.open_video(routine.path)
font = cv2.FONT_HERSHEY_SIMPLEX
framesToAverage = 300
# Keyboard stuff
visualise = True # show windows rendering video
waitTime = 15 # delay for keyboard input
paused = False
goOneFrame = False
# Window vars
scalingFactor = 0.4
capWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
capHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
resizeWidth = int(capWidth * scalingFactor)
resizeHeight = int(capHeight * scalingFactor)
# Create videos
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'DIB ')
frameOriginalOut = cv2.VideoWriter(
consts.videosRootPath + 'framesOriginal.avi', fourcc, 30.0,
(capWidth, capHeight))
frameBlurDarkVid = cv2.VideoWriter(
consts.videosRootPath + 'framesBlurDark.avi', fourcc, 30.0,
(capWidth, capHeight))
fgMaskOut = cv2.VideoWriter(consts.videosRootPath + 'fgMask.avi', fourcc,
30.0, (capWidth, capHeight))
frameFgMaskMorphedOut = cv2.VideoWriter(
consts.videosRootPath + 'frameFgMaskMorphed.avi', fourcc, 30.0,
(capWidth, capHeight))
frameFgMaskMorphedCroppedOut = cv2.VideoWriter(
consts.videosRootPath + 'frameFgMaskMorphedCropped.avi', fourcc, 30.0,
(capWidth, capHeight))
bgModelOut = cv2.VideoWriter(consts.videosRootPath + 'bgModel.avi', fourcc,
30.0, (capWidth, capHeight))
biggestContourOut = cv2.VideoWriter(
consts.videosRootPath + 'biggestContour.avi', fourcc, 30.0,
(capWidth, capHeight))
biggestContourWithHullOut = cv2.VideoWriter(
consts.videosRootPath + 'biggestContourWithHull.avi', fourcc, 30.0,
(capWidth, capHeight))
frameWithHullOut = cv2.VideoWriter(
consts.videosRootPath + 'frameWithHull&CoM.avi', fourcc, 30.0,
(capWidth, capHeight))
# For masking to the right and left of the trampoline
maskLeftBorder = int(routine.trampoline_center - (
trampoline.calcTrampolineEnds(routine.trampoline_width) / 2))
maskRightBorder = int(routine.trampoline_center + (
trampoline.calcTrampolineEnds(routine.trampoline_width) / 2))
# Create array for tiled window
processVisImgs = np.zeros(shape=(resizeHeight * 2, resizeWidth * 2, 3),
dtype=np.uint8) # (h * 3, w, CV_8UC3);
prevPersonFgMask = np.zeros(shape=(capHeight, capWidth), dtype=np.uint8)
# Create mask around trampoline
maskAboveTrmpl = np.zeros(shape=(capHeight, capWidth),
dtype=np.uint8) # cv2.CV_8U
maskAboveTrmpl[0:routine.trampoline_top,
maskLeftBorder:maskRightBorder] = 255 # [y1:y2, x1:x2]
maskBelowTrmpl = np.zeros(shape=(capHeight, capWidth), dtype=np.uint8)
maskBelowTrmpl[routine.trampoline_top:capHeight,
maskLeftBorder:maskRightBorder] = 255 # [y1:y2, x1:x2]
# trampolineAreaPx = sq_area((routine.trampoline_top, capHeight), (maskLeftBorder, maskRightBorder))
# Pick a default crop len if none saved
# cropLength = routine.crop_length if routine.crop_length else 200
# Background extractor. Ignore shadow
pKNN = cv2.createBackgroundSubtractorKNN()
pKNN.setShadowValue(0)
# Prepare background by pre-training the bg sub
prepareBgSubt(pKNN, cap, framesToAverage)
print("Press v to toggle showing visuals")
print("Press ENTER to finish, and ESC/'q' to quit")
lastContours = None # used to remember last contour if area goes too small
start_t = time.time()
while 1:
if goOneFrame or not paused:
_ret, frame = cap.read()
# frameOriginalOut.write(frame)
# TODO if mask is really noisy (area is large/ high num contours), could increase the learning rate?
# frame = cv2.bitwise_and(frame, frame, mask=maskAboveTrmpl)
frameFgMask = pKNN.apply(frame)
# fgMaskOut.write(frameFgMask)
# Crop fg mask detail to be ROI (region of interest) above the trampoline
frameFgMaskMorphed = erode_dilate(frameFgMask)
# frameFgMaskMorphedOut.write(frameFgMaskMorphed)
frameFgMaskMorphed = cv2.bitwise_and(frameFgMaskMorphed,
frameFgMaskMorphed,
mask=maskAboveTrmpl)
# frameFgMaskMorphedCroppedOut.write(frameFgMaskMorphed)
# Create mask of the common regions in this and in the prevPersonFgMask
fgMaskPrevPersonOverlap = cv2.bitwise_and(frameFgMaskMorphed,
frameFgMaskMorphed,
mask=prevPersonFgMask)
if visualise:
# Show current background model
bgModel = pKNN.getBackgroundImage()
# bgModelOut.write(bgModel)
processVisImgs[0:resizeHeight, 0:resizeWidth] = cv2.resize(
bgModel, (resizeWidth, resizeHeight))
cv2.putText(processVisImgs, 'Background Model', (10, 20), font,
0.4, (255, 255, 255))
# Show fg mask
frameFgMask4Vis = cv2.cvtColor(
cv2.resize(frameFgMask, (resizeWidth, resizeHeight)),
cv2.COLOR_GRAY2RGB)
cv2.line(
frameFgMask4Vis,
(0, int(routine.trampoline_top * scalingFactor)),
(resizeWidth, int(routine.trampoline_top * scalingFactor)),
(0, 255, 0), 1)
cv2.putText(frameFgMask4Vis, 'Foreground Mask', (10, 20), font,
0.4, (255, 255, 255))
processVisImgs[resizeHeight * 1:resizeHeight * 2, resizeWidth *
0:resizeWidth * 1] = frameFgMask4Vis
# Find contours in masked image
_img, contours, _hierarchy = cv2.findContours(
np.copy(frameFgMaskMorphed), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
print("Using last contour because none found in this frame")
contours = lastContours
# Sort DESC, so biggest contour is first
contours = sorted(contours, key=cv2.contourArea, reverse=True)
# If contour is less than min area, replace it with previous contour
if cv2.contourArea(
contours[0]
) < consts.minContourArea and lastContours is not None:
print("Using last contour because found is too small")
contours = lastContours
else:
lastContours = contours
personContourConcat, personContours, prevPersonFgMask = getPersonContour(
fgMaskPrevPersonOverlap, contours)
blobHull = cv2.convexHull(personContourConcat)
if visualise:
# Convert the foreground mask to color so the biggest can be coloured in
biggestContour = cv2.cvtColor(frameFgMaskMorphed,
cv2.COLOR_GRAY2RGB)
# Draw the biggest one in red
for contour in personContours:
cv2.drawContours(biggestContour, [contour], 0, (0, 0, 255),
cv2.FILLED)
# biggestContourOut.write(biggestContour)
# Draw the outline of the convex blobHull for the person
cv2.drawContours(biggestContour, [blobHull], 0, (0, 255, 0), 2)
# biggestContourWithHullOut.write(biggestContour)
frameWithHull = np.copy(frame)
cv2.drawContours(frameWithHull, [blobHull], 0, (0, 255, 0), 2)
# Resize and show it
biggestContour = cv2.resize(biggestContour,
(resizeWidth, resizeHeight))
cv2.putText(biggestContour, 'Blob Detection', (10, 20), font,
0.4, (255, 255, 255))
processVisImgs[resizeHeight * 1:resizeHeight * 2, resizeWidth *
1:resizeWidth * 2] = biggestContour
# cv2.imshow('personMask', personMask)
cx, cy = helper_funcs.calc_contour_center(personContourConcat)
if cx and cy:
centerPoints[int(cap.get(cv2.CAP_PROP_POS_FRAMES))] = [cx, cy]
# Save person mask so it can be used when outputting frames
# prevPersonFgMask is, at the moment, the current person fg mask. It's already been updated.
finerPersonMask = cv2.bitwise_and(frameFgMask,
frameFgMask,
mask=prevPersonFgMask)
personMasks[int(cap.get(
cv2.CAP_PROP_POS_FRAMES))] = json.dumps(
finerPersonMask.tolist())
# Get max dimension of person
_img, finerContours, _h = cv2.findContours(
np.copy(finerPersonMask), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(finerContours) > 0:
finerContours = sorted(finerContours,
key=cv2.contourArea,
reverse=True)
finerHull = cv2.convexHull(finerContours[0])
# Use finer hull because blob morph opperations will change height
hullMaxLen = getMaxHullLength(finerHull)
hullLengths[int(cap.get(cv2.CAP_PROP_POS_FRAMES))] = hullMaxLen
touchingTrmpl = isTouchingTrmpl(routine.trampoline_top,
finerHull)
trampolineTouches[int(cap.get(
cv2.CAP_PROP_POS_FRAMES))] = touchingTrmpl
if visualise:
# Show trampoline touch detection
finerPersonMask4Vis = cv2.cvtColor(finerPersonMask,
cv2.COLOR_GRAY2RGB)
cv2.drawContours(finerPersonMask4Vis, [finerHull], 0,
(0, 255, 0), 2)
if touchingTrmpl:
cv2.line(finerPersonMask4Vis,
(0, routine.trampoline_top),
(routine.video_width, routine.trampoline_top),
(0, 255, 0), 5)
else:
cv2.line(finerPersonMask4Vis,
(0, routine.trampoline_top),
(routine.video_width, routine.trampoline_top),
(0, 0, 255), 5)
finerPersonMask4Vis = cv2.resize(
finerPersonMask4Vis, (resizeWidth, resizeHeight))
cv2.imshow('finerPersonMask4Vis', finerPersonMask4Vis)
if touchingTrmpl:
cv2.line(frameWithHull, (0, routine.trampoline_top),
(routine.video_width, routine.trampoline_top),
(0, 255, 0), 3)
else:
cv2.line(frameWithHull, (0, routine.trampoline_top),
(routine.video_width, routine.trampoline_top),
(0, 0, 255), 3)
cv2.circle(frameWithHull, (cx, cy), 5, (0, 0, 255), -1)
# cv2.imshow("frameWithHull", frameWithHull)
# frameWithHullOut.write(frameWithHull)
# Show person drawing the center of mass
# cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1)
# cropLength = helper_funcs.getCropLength(hullLengths.values())
trackedPerson = highlightPerson(frame, finerPersonMask, cx,
cy, 250, frameBlurDarkVid)
trackedPerson = cv2.resize(trackedPerson, (256, 256))
cv2.imshow("Track", trackedPerson)
else:
print("Skipping center point. Couldn't find moment")
# End stuff
if visualise:
# Add the trampoline_top line
cv2.line(frame, (0, routine.trampoline_top),
(routine.video_width, routine.trampoline_top),
(0, 255, 0), 2)
frameSm = cv2.resize(frame, (resizeWidth, resizeHeight))
cv2.putText(
frameSm,
'Frame {}'.format(int(cap.get(cv2.CAP_PROP_POS_FRAMES))),
(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255))
processVisImgs[resizeHeight * 0:resizeHeight * 1,
resizeWidth * 1:resizeWidth * 2] = frameSm
cv2.imshow('Visualise Processing', processVisImgs)
if saveReportImages: # p (print). Saves image for the report
cv2.imwrite(consts.thesisImgPath + "bgsub_1.png", frame)
# If we went one frame, stop from going another
if goOneFrame:
goOneFrame = False
k = cv2.waitKey(waitTime) & 0xff
if k == ord('v'):
visualise = not visualise
elif k == ord('k'): # play pause
paused = not paused
elif k == ord('j'): # prev frame
cap.set(cv2.CAP_PROP_POS_FRAMES,
cap.get(cv2.CAP_PROP_POS_FRAMES) - 2)
goOneFrame = True
elif k == ord('l'): # next frame
goOneFrame = True
elif k == ord('.'): # speed up
waitTime -= 5
print(waitTime)
elif k == ord(','): # slow down
waitTime += 5
print(waitTime)
elif ord('0') <= k <= ord('9'):
num = k - ord('0')
frameToJumpTo = (cap.get(cv2.CAP_PROP_FRAME_COUNT) / 10) * num
cap.set(cv2.CAP_PROP_POS_FRAMES, frameToJumpTo)
goOneFrame = True
elif k == ord('u'):
routine.use = 0 if routine.use else 1
db.commit()
print("use updated to", routine.use)
elif k == ord('\n') or k == ord('\r'): # return/enter key
break
elif k == ord('q') or k == 27: # q/ESC
print("Exiting...")
exit()
elif k == ord('n'):
joinNearbyContours = not joinNearbyContours
if saveReportImages:
saveReportImages = False
if k == ord('p'):
saveReportImages = True
if paused:
goOneFrame = True
# Finish playing the video when we get to the end.
if cap.get(cv2.CAP_PROP_POS_FRAMES) == cap.get(
cv2.CAP_PROP_FRAME_COUNT):
break
# Calc fps
end_t = time.time()
time_taken = end_t - start_t
start_t = end_t
if time_taken != 0:
fps = 1. / time_taken
print('Loop fps: {}'.format(fps))
cap.release()
# fgMaskOut.release()
cv2.destroyAllWindows()
return centerPoints, hullLengths, trampolineTouches, personMasks