def play_dataset_video(self, is_train, video_index, show=True):
self.scd = PgdSkeleton(Path.home() / 'PoliceGestureLong', is_train, self.img_size)
res = self.scd[video_index]
print('Playing %s' % res[PG.VIDEO_NAME])
coord_norm_FXJ = res[PG.COORD_NORM] # Shape: F,X,J
coord_norm_FJX = np.transpose(coord_norm_FXJ, (0, 2, 1)) # FJX
coord = coord_norm_FJX * np.array(self.img_size)
img_shape = self.img_size[::-1] + (3,)
kps = [KeypointsOnImage.from_xy_array(coord_JX, shape=img_shape) for coord_JX in coord] # (frames, KeyOnImage)
cap = cv2.VideoCapture(str(res[PG.VIDEO_PATH]))
v_size = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
v_fps = int(cap.get(cv2.CAP_PROP_FPS))
duration = int(1000/(v_fps*4))
gestures = [] # Full video gesture recognition results
for n in range(v_size):
gdict = self.gpred.from_skeleton(coord_norm_FXJ[n][np.newaxis])
gesture = gdict[PG.OUT_ARGMAX]
gestures.append(gesture)
if not show:
continue
ret, img = cap.read()
re_img = cv2.resize(img, self.img_size)
ges_name = self.gesture_dict[gesture]
re_img = draw_text(re_img, 50, 100, ges_name, (255, 50, 50), size=40)
pOnImg = kps[n]
img_kps = pOnImg.draw_on_image(re_img)
if self.is_unittest:
break
cv2.imshow("Play saved keypoint results", img_kps)
cv2.waitKey(duration)
cap.release()
gestures = np.array(gestures, np.int)
res[PG.PRED_GESTURES] = gestures
print('The prediction of video ', res[PG.VIDEO_NAME], ' is completed')
return res
def play_custom_video(self, video_path):
rkr = ResizeKeepRatio((512, 512))
if video_path is None:
cap = cv2.VideoCapture(0)
if not cap.isOpened():
raise IOError('Failed to open camera.')
else:
cap = cv2.VideoCapture(str(video_path))
v_fps = int(cap.get(cv2.CAP_PROP_FPS))
if v_fps != 15:
warn('Suggested video frame rate is 15, currently %d, which may impact accuracy' % v_fps)
duration = 10
while True:
ret, img = cap.read()
if not ret:
break
re_img, _, _ = rkr.resize(img, np.zeros((2,)), np.zeros((4,)))
gdict = self.gpred.from_img(re_img)
gesture = gdict[PG.OUT_ARGMAX]
coord_norm_FXJ = gdict[PG.COORD_NORM]
coord_norm_FJX = np.transpose(coord_norm_FXJ, (0, 2, 1))
coord_FJX = coord_norm_FJX * np.array(self.img_size)
koi = KeypointsOnImage.from_xy_array(coord_FJX[0], shape=re_img.shape)
re_img = koi.draw_on_image(re_img)
ges_name = self.gesture_dict[gesture]
re_img = draw_text(re_img, 50, 100, ges_name, (255, 50, 50), size=40)
if self.is_unittest:
break
cv2.imshow("Play saved keypoint results", re_img)
cv2.waitKey(duration)
cap.release()
def play_custom_image(self, img_path):
rkr = ResizeKeepRatio((512, 512))
img = cv2.imread(img_path)
re_img, _, _ = rkr.resize(img, np.zeros((2,)), np.zeros((4,)))
gdict = self.gpred.from_img(re_img)
gesture = gdict[PG.OUT_ARGMAX]
coord_norm_FXJ = gdict[PG.COORD_NORM]
coord_norm_FJX = np.transpose(coord_norm_FXJ, (0, 2, 1))
coord_FJX = coord_norm_FJX * np.array(self.img_size)
koi = KeypointsOnImage.from_xy_array(coord_FJX[0], shape=re_img.shape)
re_img = koi.draw_on_image(re_img)
ges_name = self.gesture_dict[gesture]
re_img = draw_text(re_img, 50, 100, ges_name, (255, 50, 50), size=40)
cv2.imshow("Play saved keypoint results", re_img)
def play_custom_video(self, video_path):
cap = cv2.VideoCapture(str(video_path))
v_size = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
v_fps = int(cap.get(cv2.CAP_PROP_FPS))
duration = 10
for n in range(v_size):
ret, img = cap.read()
re_img = cv2.resize(img, self.img_size)
gdict = self.gpred.from_img(re_img)
gesture = gdict[PG.OUT_ARGMAX]
# Keypoints on image
coord_norm_FXJ = gdict[PG.COORD_NORM]
coord_norm_FJX = np.transpose(coord_norm_FXJ, (0, 2, 1)) # FJX
coord_FJX = coord_norm_FJX * np.array(self.img_size)
koi = KeypointsOnImage.from_xy_array(coord_FJX[0], shape=re_img.shape)
re_img = koi.draw_on_image(re_img)
# Gesture name on image
ges_name = self.gesture_dict[gesture]
re_img = draw_text(re_img, 50, 100, ges_name, (255, 50, 50), size=40)
cv2.imshow("Play saved keypoint results", re_img)
cv2.waitKey(duration)
def play_dataset_video(self, is_train, video_index):
self.scd = SkeletonCoordsDataset(Path.home() / 'PoliceGestureLong', is_train, self.img_size)
res = self.scd[video_index]
coord_norm_FXJ = res[PG.COORD_NORM] # Shape: F,X,J
coord_norm_FJX = np.transpose(coord_norm_FXJ, (0, 2, 1)) # FJX
coord = coord_norm_FJX * np.array(self.img_size)
img_shape = self.img_size[::-1] + (3,)
kps = [KeypointsOnImage.from_xy_array(coord_JX, shape=img_shape) for coord_JX in coord] # (frames, KeyOnImage)
cap = cv2.VideoCapture(str(res[PG.VIDEO_PATH]))
v_size = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
v_fps = int(cap.get(cv2.CAP_PROP_FPS))
duration = int(1000/(v_fps*4))
for n in range(v_size):
ret, img = cap.read()
re_img = cv2.resize(img, self.img_size)
gdict = self.gpred.from_skeleton(coord_norm_FXJ[n][np.newaxis])
gesture = gdict[PG.OUT_ARGMAX]
ges_name = self.gesture_dict[gesture]
re_img = draw_text(re_img, 50, 100, ges_name, (255, 50, 50), size=40)
pOnImg = kps[n]
img_kps = pOnImg.draw_on_image(re_img)
cv2.imshow("Play saved keypoint results", img_kps)
cv2.waitKey(duration)
