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HandTrackerRenderer.py
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HandTrackerRenderer.py
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import cv2
import numpy as np
LINES_HAND = [[0,1],[1,2],[2,3],[3,4],
[0,5],[5,6],[6,7],[7,8],
[5,9],[9,10],[10,11],[11,12],
[9,13],[13,14],[14,15],[15,16],
[13,17],[17,18],[18,19],[19,20],[0,17]]
# LINES_BODY to draw the body skeleton when Body Pre Focusing is used
LINES_BODY = [[4,2],[2,0],[0,1],[1,3],
[10,8],[8,6],[6,5],[5,7],[7,9],
[6,12],[12,11],[11,5],
[12,14],[14,16],[11,13],[13,15]]
class HandTrackerRenderer:
def __init__(self,
tracker,
output=None):
self.tracker = tracker
# Rendering flags
if self.tracker.use_lm:
self.show_pd_box = False
self.show_pd_kps = False
self.show_rot_rect = False
self.show_handedness = 0
self.show_landmarks = True
self.show_scores = False
self.show_gesture = self.tracker.use_gesture
else:
self.show_pd_box = True
self.show_pd_kps = False
self.show_rot_rect = False
self.show_scores = False
self.show_xyz_zone = self.show_xyz = self.tracker.xyz
self.show_fps = True
self.show_body = False # self.tracker.body_pre_focusing is not None
self.show_inferences_status = False
if output is None:
self.output = None
else:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
self.output = cv2.VideoWriter(output,fourcc,self.tracker.video_fps,(self.tracker.img_w, self.tracker.img_h))
def norm2abs(self, x_y):
x = int(x_y[0] * self.tracker.frame_size - self.tracker.pad_w)
y = int(x_y[1] * self.tracker.frame_size - self.tracker.pad_h)
return (x, y)
def draw_hand(self, hand):
if self.tracker.use_lm:
# (info_ref_x, info_ref_y): coords in the image of a reference point
# relatively to which hands information (score, handedness, xyz,...) are drawn
info_ref_x = hand.landmarks[0,0]
info_ref_y = np.max(hand.landmarks[:,1])
# thick_coef is used to adapt the size of the draw landmarks features according to the size of the hand.
thick_coef = hand.rect_w_a / 400
if hand.lm_score > self.tracker.lm_score_thresh:
if self.show_rot_rect:
cv2.polylines(self.frame, [np.array(hand.rect_points)], True, (0,255,255), 2, cv2.LINE_AA)
if self.show_landmarks:
lines = [np.array([hand.landmarks[point] for point in line]).astype(np.int) for line in LINES_HAND]
if self.show_handedness == 3:
color = (0,255,0) if hand.handedness > 0.5 else (0,0,255)
else:
color = (255, 0, 0)
cv2.polylines(self.frame, lines, False, color, int(1+thick_coef*3), cv2.LINE_AA)
radius = int(1+thick_coef*5)
if self.tracker.use_gesture:
# color depending on finger state (1=open, 0=close, -1=unknown)
color = { 1: (0,255,0), 0: (0,0,255), -1:(0,255,255)}
cv2.circle(self.frame, (hand.landmarks[0][0], hand.landmarks[0][1]), radius, color[-1], -1)
for i in range(1,5):
cv2.circle(self.frame, (hand.landmarks[i][0], hand.landmarks[i][1]), radius, color[hand.thumb_state], -1)
for i in range(5,9):
cv2.circle(self.frame, (hand.landmarks[i][0], hand.landmarks[i][1]), radius, color[hand.index_state], -1)
for i in range(9,13):
cv2.circle(self.frame, (hand.landmarks[i][0], hand.landmarks[i][1]), radius, color[hand.middle_state], -1)
for i in range(13,17):
cv2.circle(self.frame, (hand.landmarks[i][0], hand.landmarks[i][1]), radius, color[hand.ring_state], -1)
for i in range(17,21):
cv2.circle(self.frame, (hand.landmarks[i][0], hand.landmarks[i][1]), radius, color[hand.little_state], -1)
else:
if self.show_handedness == 2:
color = (0,255,0) if hand.handedness > 0.5 else (0,0,255)
elif self.show_handedness == 3:
color = (255, 0, 0)
else:
color = (0,128,255)
for x,y in hand.landmarks[:,:2]:
cv2.circle(self.frame, (int(x), int(y)), radius, color, -1)
if self.show_handedness == 1:
cv2.putText(self.frame, f"{hand.label.upper()} {hand.handedness:.2f}",
(info_ref_x-90, info_ref_y+40),
cv2.FONT_HERSHEY_PLAIN, 2, (0,255,0) if hand.handedness > 0.5 else (0,0,255), 2)
if self.show_scores:
cv2.putText(self.frame, f"Landmark score: {hand.lm_score:.2f}",
(info_ref_x-90, info_ref_y+110),
cv2.FONT_HERSHEY_PLAIN, 2, (255,255,0), 2)
if self.tracker.use_gesture and self.show_gesture:
cv2.putText(self.frame, hand.gesture, (info_ref_x-20, info_ref_y-50),
cv2.FONT_HERSHEY_PLAIN, 3, (255,255,255), 3)
if hand.pd_box is not None:
box = hand.pd_box
box_tl = self.norm2abs((box[0], box[1]))
box_br = self.norm2abs((box[0]+box[2], box[1]+box[3]))
if self.show_pd_box:
cv2.rectangle(self.frame, box_tl, box_br, (0,255,0), 2)
if self.show_pd_kps:
for i,kp in enumerate(hand.pd_kps):
x_y = self.norm2abs(kp)
cv2.circle(self.frame, x_y, 6, (0,0,255), -1)
cv2.putText(self.frame, str(i), (x_y[0], x_y[1]+12), cv2.FONT_HERSHEY_PLAIN, 1.5, (0,255,0), 2)
if self.show_scores:
if self.tracker.use_lm:
x, y = info_ref_x - 90, info_ref_y + 80
else:
x, y = box_tl[0], box_br[1]+60
cv2.putText(self.frame, f"Palm score: {hand.pd_score:.2f}",
(x, y),
cv2.FONT_HERSHEY_PLAIN, 2, (255,255,0), 2)
if self.show_xyz:
if self.tracker.use_lm:
x0, y0 = info_ref_x - 40, info_ref_y + 40
else:
x0, y0 = box_tl[0], box_br[1]+20
cv2.rectangle(self.frame, (x0,y0), (x0+100, y0+85), (220,220,240), -1)
cv2.putText(self.frame, f"X:{hand.xyz[0]/10:3.0f} cm", (x0+10, y0+20), cv2.FONT_HERSHEY_PLAIN, 1, (20,180,0), 2)
cv2.putText(self.frame, f"Y:{hand.xyz[1]/10:3.0f} cm", (x0+10, y0+45), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0), 2)
cv2.putText(self.frame, f"Z:{hand.xyz[2]/10:3.0f} cm", (x0+10, y0+70), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 2)
if self.show_xyz_zone:
# Show zone on which the spatial data were calculated
cv2.rectangle(self.frame, tuple(hand.xyz_zone[0:2]), tuple(hand.xyz_zone[2:4]), (180,0,180), 2)
def draw_body(self, body):
lines = [np.array([body.keypoints[point] for point in line]) for line in LINES_BODY if body.scores[line[0]] > self.tracker.body_score_thresh and body.scores[line[1]] > self.tracker.body_score_thresh]
cv2.polylines(self.frame, lines, False, (255, 144, 30), 2, cv2.LINE_AA)
def draw_bag(self, bag):
if self.show_inferences_status:
# Draw inferences status
h = self.frame.shape[0]
u = h // 10
status=""
if bag.get("bpf_inference", 0):
cv2.rectangle(self.frame, (u, 8*u), (2*u, 9*u), (255,144,30), -1)
if bag.get("pd_inference", 0):
cv2.rectangle(self.frame, (2*u, 8*u), (3*u, 9*u), (0,255,0), -1)
nb_lm_inferences = bag.get("lm_inference", 0)
if nb_lm_inferences:
cv2.rectangle(self.frame, (3*u, 8*u), ((3+nb_lm_inferences)*u, 9*u), (0,0,255), -1)
body = bag.get("body", False)
if body and self.show_body:
# Draw skeleton
self.draw_body(body)
# Draw Movenet smart cropping rectangle
cv2.rectangle(self.frame, (body.crop_region.xmin, body.crop_region.ymin), (body.crop_region.xmax, body.crop_region.ymax), (0,255,255), 2)
# Draw focus zone
focus_zone= bag.get("focus_zone", None)
if focus_zone:
cv2.rectangle(self.frame, tuple(focus_zone[0:2]), tuple(focus_zone[2:4]), (0,255,0),2)
def draw(self, frame, hands, bag={}):
self.frame = frame
if bag:
self.draw_bag(bag)
for hand in hands:
self.draw_hand(hand)
return self.frame
def exit(self):
if self.output:
self.output.release()
cv2.destroyAllWindows()
def waitKey(self, delay=1):
if self.show_fps:
self.tracker.fps.draw(self.frame, orig=(50,50), size=1, color=(240,180,100))
cv2.imshow("Hand tracking", self.frame)
if self.output:
self.output.write(self.frame)
key = cv2.waitKey(delay)
if key == 32:
# Pause on space bar
key = cv2.waitKey(0)
if key == ord('s'):
print("Snapshot saved in snapshot.jpg")
cv2.imwrite("snapshot.jpg", self.frame)
elif key == ord('1'):
self.show_pd_box = not self.show_pd_box
elif key == ord('2'):
self.show_pd_kps = not self.show_pd_kps
elif key == ord('3'):
self.show_rot_rect = not self.show_rot_rect
elif key == ord('4') and self.tracker.use_lm:
self.show_landmarks = not self.show_landmarks
elif key == ord('5') and self.tracker.use_lm:
self.show_handedness = (self.show_handedness + 1) % 4
elif key == ord('6'):
self.show_scores = not self.show_scores
elif key == ord('7') and self.tracker.use_lm:
if self.tracker.use_gesture:
self.show_gesture = not self.show_gesture
elif key == ord('8'):
if self.tracker.xyz:
self.show_xyz = not self.show_xyz
elif key == ord('9'):
if self.tracker.xyz:
self.show_xyz_zone = not self.show_xyz_zone
elif key == ord('f'):
self.show_fps = not self.show_fps
elif key == ord('b'):
try:
if self.tracker.body_pre_focusing:
self.show_body = not self.show_body
except:
pass
elif key == ord('s'):
self.show_inferences_status = not self.show_inferences_status
return key