def cv_init(self):
WINDOW = "Hand Tracking"
PALM_MODEL_PATH = "models/palm_detection_without_custom_op.tflite"
LANDMARK_MODEL_PATH = "models/hand_landmark.tflite"
ANCHORS_PATH = "models/anchors.csv"
detector = HandTracker(PALM_MODEL_PATH,
LANDMARK_MODEL_PATH,
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1.3)
sign_classifier = load_model('models/model2.h5')
SIGNS = [
'one', 'two', 'three', 'four', 'five', 'ok', 'rock', 'thumbs_up'
]
SIGNS_dict = {
'one': 1,
'two': 2,
'three': 3,
'four': 4,
'five': 5,
'ok': 6,
'rock': 7,
'thumbs_up': 8
}
POINT_COLOR = (0, 255, 0)
CONNECTION_COLOR = (255, 0, 0)
THICKNESS = 2
cv2.namedWindow(WINDOW)
capture = cv2.VideoCapture(1)
if capture.isOpened():
hasFrame, frame = capture.read()
else:
hasFrame = False
self.connections = [(0, 1), (1, 2), (2, 3), (3, 4), (5, 6), (6, 7),
(7, 8), (9, 10), (10, 11), (11, 12), (13, 14),
(14, 15), (15, 16), (17, 18), (18, 19), (19, 20),
(0, 5), (5, 9), (9, 13), (13, 17), (0, 17)]
def __init__(self):
self.palm_model_path = "./models/palm_detection_without_custom_op.tflite"
self.landmark_model_path = "./models/hand_landmark.tflite"
self.anchors_path = "./models/anchors.csv"
self.detector = HandTracker(self.palm_model_path,
self.landmark_model_path,
self.anchors_path,
box_shift=0.2,
box_enlarge=1.3)
self.POINT_COLOR = (0, 255, 0)
self.CONNECTION_COLOR = (255, 0, 0)
self.THICKNESS = 2
self.connections = [(0, 1), (1, 2), (2, 3), (3, 4), (5, 6), (6, 7),
(7, 8), (9, 10), (10, 11), (11, 12), (13, 14),
(14, 15), (15, 16), (17, 18), (18, 19), (19, 20),
(0, 5), (5, 9), (9, 13), (13, 17), (0, 17)]
self.palm_pos = [0, 0]
self.gesture = "first"
self.palm_depth = 0
# 4 | | | |
# | 6 10 14 18
# 3 | | | |
# | 5---9---13--17
# 2 \ /
# \ \ /
# 1 \ /
# \ \ /
# ------0-
connections = [(0, 1), (1, 2), (2, 3), (3, 4), (5, 6), (6, 7), (7, 8), (9, 10),
(10, 11), (11, 12), (13, 14), (14, 15), (15, 16), (17, 18),
(18, 19), (19, 20), (0, 5), (5, 9), (9, 13), (13, 17), (0, 17)]
detector = HandTracker(PALM_MODEL_PATH,
LANDMARK_MODEL_PATH,
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1.3)
def get_size_pulm(pulm_key_point):
#calculation size of pulm
k_pulm = len(pulm_key_point)
distance_pulm = np.zeros(k_pulm - 1)
size_pulm = 0
for i in range(k_pulm - 1):
x1 = points[i][0]
y1 = points[i][1]
x2 = points[i + 1][0]
y2 = points[i + 1][1]
distance_pulm[i] = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
def main():
cv2.namedWindow(WINDOW)
capture = cv2.VideoCapture(0)
if capture.isOpened():
hasFrame, frame = capture.read()
else:
hasFrame = False
detector = HandTracker(PALM_MODEL_PATH,
LANDMARK_MODEL_PATH,
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1.3)
while hasFrame:
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
scale = np.array(np.max(frame.shape[:2]) / 256.0)
padding = [0, 280]
img_pad, img_norm, pad = detector.preprocess_img(image)
source, keypoints, debug_info = detector.detect_hand(img_norm)
if debug_info is not None:
candidate_detect = debug_info["detection_candidates"]
candidate_anchors = debug_info["anchor_candidates"]
selected_box_id = debug_info["selected_box_id"]
if DRAW_ANCHORS and debug_info is not None:
for anchor in candidate_anchors:
dx, dy = anchor[:2] * 256
w, h = anchor[
2:] * 256 * 0.2 # no idea of 0.2 is the correct size multiplication
box = box_from_dimensions(dx - (w / 2), dy - (h / 2), h, w)
box *= scale
box -= padding
frame = draw_box(frame, box, color=(200, 0, 0))
if DRAW_DETECTION_BOXES and debug_info is not None:
for i, detection in enumerate(candidate_detect):
dx, dy, w, h = detection[:4]
center_wo_offst = candidate_anchors[i, :2] * 256
box = box_from_dimensions(dx - (w / 2), dy - (h / 2), h, w)
box += center_wo_offst
box *= scale
box -= padding
frame = draw_box(frame, box)
if DRAW_HAND_KEYPOINTS and debug_info is not None:
detection = candidate_detect[selected_box_id]
center_wo_offst = candidate_anchors[i, :2] * 256
hand_key_points = center_wo_offst + detection[4:].reshape(-1, 2)
for key_point in hand_key_points:
key_point *= scale
key_point -= padding
cv2.circle(frame,
tuple(key_point.astype("int")),
color=(255, 255, 255),
radius=5,
thickness=2)
if DRAW_BEST_DETECTION_BOX_NMS and debug_info is not None:
detection = candidate_detect[selected_box_id]
dx, dy, w, h = detection[:4]
center_wo_offst = candidate_anchors[selected_box_id, :2] * 256
box = box_from_dimensions(dx - (w / 2), dy - (h / 2), h, w)
box += center_wo_offst
box *= scale
box -= padding
frame = draw_box(frame, box, color=(0, 0, 255))
cv2.imshow(WINDOW, frame)
hasFrame, frame = capture.read()
key = cv2.waitKey(20)
if key == ESCAPE_KEY_CODE:
break
capture.release()
cv2.destroyAllWindows()
def gesture_function():
cv2.namedWindow(WINDOW)
capture = cv2.VideoCapture(1)
if capture.isOpened():
hasFrame, frame = capture.read()
else:
hasFrame = False
# 8 12 16 20
# | | | |
# 7 11 15 19
# 4 | | | |
# | 6 10 14 18
# 3 | | | |
# | 5---9---13--17
# 2 \ /
# \ \ /
# 1 \ /
# \ \ /
# ------0-
connections = [(0, 1), (1, 2), (2, 3), (3, 4), (5, 6), (6, 7), (7, 8),
(9, 10), (10, 11), (11, 12), (13, 14), (14, 15), (15, 16),
(17, 18), (18, 19), (19, 20), (0, 5), (5, 9), (9, 13),
(13, 17), (0, 17)]
detector = HandTracker(PALM_MODEL_PATH,
LANDMARK_MODEL_PATH,
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1.3)
def get_euclidean_distance(ax, ay, bx, by):
dist = ((ax - bx)**2) + ((ay - by)**2)
return math.sqrt(dist)
def isThumbNearFirstFinger(p1, p2):
distance = get_euclidean_distance(p1[0], p1[1], p2[0], p2[1])
return distance < 0.1
gesture = [0 for i in range(9)]
global_vars.acquire()
while hasFrame and global_vars.do_react:
global_vars.release()
thumbIsOpen = False
firstFingerIsOpen = False
secondFingerIsOpen = False
thirdFingerIsOpen = False
fourthFingerIsOpen = False
k = 0
landmarkList = [(0, 0)] * 21
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
points, _ = detector(image)
if points is not None:
for point in points:
x, y = point
landmarkList[k] = (x, y)
k = k + 1
cv2.circle(frame, (int(x), int(y)), THICKNESS * 2, POINT_COLOR,
THICKNESS)
for connection in connections:
x0, y0 = points[connection[0]]
x1, y1 = points[connection[1]]
cv2.line(frame, (int(x0), int(y0)), (int(x1), int(y1)),
CONNECTION_COLOR, THICKNESS)
j = landmarkList[2][0]
if landmarkList[3][0] < j and landmarkList[4][0] < j:
thumbIsOpen = True
j = landmarkList[6][1]
if landmarkList[7][1] < j and landmarkList[8][1] < j:
firstFingerIsOpen = True
j = landmarkList[10][1]
if landmarkList[11][1] < j and landmarkList[12][1] < j:
secondFingerIsOpen = True
j = landmarkList[14][1]
if landmarkList[15][1] < j and landmarkList[16][1] < j:
thirdFingerIsOpen = True
j = landmarkList[18][1]
if landmarkList[19][1] < j and landmarkList[20][1] < j:
fourthFingerIsOpen = True
if thumbIsOpen and firstFingerIsOpen and secondFingerIsOpen and thirdFingerIsOpen and fourthFingerIsOpen:
gesture[5] += 1 # "FIVE"
elif not thumbIsOpen and firstFingerIsOpen and secondFingerIsOpen and thirdFingerIsOpen and fourthFingerIsOpen:
gesture[4] += 1 # "FOUR"
elif thumbIsOpen and firstFingerIsOpen and secondFingerIsOpen and not thirdFingerIsOpen and not fourthFingerIsOpen:
gesture[3] += 1 # "THREE"
elif thumbIsOpen and firstFingerIsOpen and not secondFingerIsOpen and not thirdFingerIsOpen and not fourthFingerIsOpen:
gesture[2] += 1 # "TWO"
elif not thumbIsOpen and firstFingerIsOpen and not secondFingerIsOpen and not thirdFingerIsOpen and not fourthFingerIsOpen:
gesture[1] += 1 # "ONE"
elif not thumbIsOpen and firstFingerIsOpen and secondFingerIsOpen and not thirdFingerIsOpen and not fourthFingerIsOpen:
gesture[0] += 1 # "YEAH"
elif not thumbIsOpen and firstFingerIsOpen and not secondFingerIsOpen and not thirdFingerIsOpen and fourthFingerIsOpen:
gesture[6] += 1 # "ROCK"
elif thumbIsOpen and firstFingerIsOpen and not secondFingerIsOpen and not thirdFingerIsOpen and fourthFingerIsOpen:
gesture[7] += 1 # "SPIDERMAN"
elif not thumbIsOpen and not firstFingerIsOpen and not secondFingerIsOpen and not thirdFingerIsOpen and not fourthFingerIsOpen:
gesture[8] += 1 # "FIST"
print(gesture)
if any([i >= 3 for i in gesture]):
cur_gesture = gesture.index(3)
print("Detected gesture: ", cur_gesture)
global_vars.acquire()
# print("inside lock from gesture!")
cur_action = global_vars.gesture_assignments[cur_gesture]
if cur_action != 7:
try:
# print(global_vars.gesture_assignments)
zoom_bridge_functions.zoom_function_wrap(cur_action)
except (pywinauto.findwindows.ElementNotFoundError,
pywinauto.findbestmatch.MatchError, AttributeError,
RuntimeError) as e:
print(e.args)
else:
global_vars.do_live_transcribe = not global_vars.do_live_transcribe
global_vars.release()
# print("outside lock from gesture!")
gesture = [0 for i in range(9)]
# cv2.imshow(WINDOW, frame)
hasFrame, frame = capture.read()
key = cv2.waitKey(1)
if key == 27:
break
time.sleep(0.01)
global_vars.acquire()
capture.release()
cv2.destroyAllWindows()
def handtracking():
#Initialize webcam capture
cv2.namedWindow(WINDOW)
capture = cv2.VideoCapture(0)
record = False
if capture.isOpened():
hasFrame, frame = capture.read()
else:
hasFrame = False
# 8 12 16 20
# | | | |
# 7 11 15 19
# 4 | | | |
# | 6 10 14 18
# 3 | | | |
# | 5---9---13--17
# 2 \ /
# \ \ /
# 1 \ /
# \ \ /
# ------0-
#All connections made to detect hand from the diagram above
connections = [(1, 2), (2, 3), (3, 4), (5, 6), (6, 7), (7, 8), (9, 10),
(10, 11), (11, 12), (13, 14), (14, 15), (15, 16), (17, 18),
(18, 19), (19, 20), (5, 9), (9, 13), (13, 17), (0, 5),
(0, 17), (0, 9), (0, 13), (0, 17), (0, 2), (0, 1), (1, 5),
(0, 6), (0, 10), (0, 14), (0, 18), (1, 9), (1, 13), (1, 17),
(0, 5), (0, 17), (5, 9), (9, 13), (13, 17)]
#Palm connections
palm = [(0, 9), (0, 13), (0, 17), (0, 1), (1, 5), (1, 9), (1, 13), (1, 17),
(0, 5), (0, 17), (5, 9), (9, 13), (13, 17)]
detector = HandTracker(PALM_MODEL_PATH,
LANDMARK_MODEL_PATH,
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1.3)
#Initialize Video Writer to save video clip for model to predict on
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)
size = (width, height)
fourcc = cv2.VideoWriter_fourcc(*'FMP4')
out = cv2.VideoWriter('testvideo.mp4', fourcc, 5, size)
while hasFrame:
#Convert images to different color channels for processing
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
#Masking image to isolate the hand
hsv_color1 = np.asarray([0, 0, 255])
hsv_color2 = np.asarray([76, 36, 133])
mask = cv2.inRange(image_hsv, hsv_color1, hsv_color2)
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
if record == False:
cv2.putText(mask, "Press Space To Start Recording or Q to quit",
(50, 50), 0, 1, (255, 255, 255), 2, cv2.LINE_AA)
#Detect points on hand
points, _ = detector(image)
if points is not None:
for point in points:
x, y = point
#Join points using connections
for connection in connections:
x0, y0 = points[connection[0]]
x1, y1 = points[connection[1]]
if connection in palm:
cv2.line(mask, (int(x0), int(y0)), (int(x1), int(y1)),
CONNECTION_COLOR, THICKNESS * 5)
else:
cv2.line(mask, (int(x0), int(y0)), (int(x1), int(y1)),
CONNECTION_COLOR, THICKNESS * 3)
cv2.namedWindow("Sign2Text", cv2.WINDOW_NORMAL)
horizontal_concat = np.concatenate((mask, frame), axis=1)
cv2.resizeWindow('Sign2Text', 1000, 1000)
cv2.imshow('Sign2Text', horizontal_concat)
hasFrame, frame = capture.read()
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
if key % 256 == 32:
record = True
cv2.rectangle(mask, (0, 0), (1240, 100), (0, 0, 0), -1)
if record:
out.write(mask)
capture.release()
out.release()
def hand_tracking(frame, detector):
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
points, bbox = detector(image)
return (frame, bbox)
cv2.namedWindow("New")
PALM_MODEL_PATH = "models/palm_detection_without_custom_op.tflite"
CONNECTION_COLOR = (0, 255, 0)
THICKNESS = 4
ANCHORS_PATH = "models/anchors.csv"
detector = HandTracker("false",
PALM_MODEL_PATH,
"",
ANCHORS_PATH,
box_shift=0.2,
box_enlarge=1)
cap = cv2.VideoCapture(0)
results, answer = run(n=0) # find puzzles with as few givens as possible.
puzzle = best(results) # use the best one of those puzzles.
#print(puzzle)
if cap.isOpened():
hasFrame, frame = cap.read()
else:
hasFrame = False
bbox = []