def hand_skel(self, image_path, is_left=False):
from openpose import pyopenpose as op
# Read image and face rectangle locations
img = cv2.imread(image_path)
if img is None:
return None
height, width = img.shape[:2]
top, bottom, left, right = get_square_padding(img)
img = squarify(img, (top, bottom, left, right))
hand_rect = op.Rectangle(0.0, 0.0, img.shape[0], img.shape[0])
null_rect = op.Rectangle(0.0, 0.0, 0.0, 0.0)
if is_left:
hand_rects = [[hand_rect, null_rect]]
else:
hand_rects = [[null_rect, hand_rect]]
datum = op.Datum()
datum.cvInputData = img
datum.handRectangles = hand_rects
self.op_wrap.emplaceAndPop(op.VectorDatum([datum]))
if is_left:
kps = datum.handKeypoints[0][0]
else:
kps = datum.handKeypoints[1][0]
if left:
kps[:, 0] -= left
elif top:
kps[:, 1] -= top
return kps, width, height
def HandKeypoint_getKeypoints(self, HandImage, box):
#
# implementCODE
#
# keypoints = KeypointType()
arr = np.fromstring(HandImage.image, np.uint8)
frame = np.reshape(
arr, (HandImage.height, HandImage.width, HandImage.depth))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
print('Input Data Recieved')
box = np.array(box)
## Creating openpose rectangle
hands_rectangles = [[
self.box2oprectangle(box),
op.Rectangle(0., 0., 0., 0.)
]]
# Create new datum
datum = op.Datum()
datum.cvInputData = frame
datum.handRectangles = hands_rectangles
# Process and display image
self.openpose_wrapper.emplaceAndPop([datum])
if datum.handKeypoints[0].shape == ():
# if there were no detections
hand_keypoints = None
else:
hand_keypoints = datum.handKeypoints[0][0]
return hand_keypoints
def face_region_zoom_out(self, cv_image, rect):
rect_coords = self.rect_to_bbox(rect)
xleft = rect_coords[0] - 275
yleft = rect_coords[1] - 100
xright = rect_coords[2] - 25
yright = rect_coords[1] - 100
cv_image = cv2.rectangle(cv_image, (xleft, yleft),
(xleft + 300, yleft + 300), (255, 255, 0))
cv_image = cv2.rectangle(cv_image, (xright, yright),
(xright + 300, yright + 300), (255, 0, 0))
# cv2.imshow("Hand Region", cv_image)
cv2.waitKey(1)
handRectangle = [
op.Rectangle(xleft, yleft, 300, 300),
op.Rectangle(xright, yright, 300, 300)
]
return handRectangle
def hand_pose(model_dir, left=0):
params = {'model_folder': model_dir}
params['net_resolution'] = '240x192'
#params['disable_blending'] = True
#params['number_people_max'] = 1
params['hand'] = True
#params['face'] = True
params['body'] = 0
params['hand_detector'] = 2
hands_boxes = [ # left & right hands: only 1-person
[pyop.Rectangle(0, 0, 0, 0),
pyop.Rectangle(0, 0, 640, 640)]
]
#opWrapper = pyop.WrapperPython(pyop.ThreadManagerMode.Synchronous)
#opWrapper.configure(params); opWrapper.execute() # webcam
'''opWrapper = pyop.WrapperPython(pyop.ThreadManagerMode.AsynchronousOut)
opWrapper.configure(params); opWrapper.start() # webcam
while cv2.waitKey(5)!=27: # faster
t = time(); x = pyop.VectorDatum()
if not opWrapper.waitAndPop(x): continue
x = x[0]; im = x.cvOutputData; fps = 'FPS=%.1f'%(1/(time()-t))
im = cv2.putText(im, fps, (5,20), 4, 0.7, (0,255,255), 1, 16)
cv2.imshow('OpenPose', im) # hand_standalone unsupported
cv2.destroyAllWindows()#'''
opWrapper = pyop.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
x = pyop.Datum()
cap = cv2.VideoCapture(-1)
hand_boxes = hand_box(cap, left)
while cv2.waitKey(5) != 27: # slower
t = time()
_, im = cap.read()
x.cvInputData = im
if params['hand_detector'] == 2: x.handRectangles = hand_boxes
opWrapper.emplaceAndPop(pyop.VectorDatum([x]))
im = x.cvOutputData
fps = 'FPS=%.1f' % (1 / (time() - t))
im = cv2.putText(im, fps, (5, 20), 4, 0.7, (0, 255, 255), 1, 16)
cv2.imshow('OpenPose', RSZ(im, 1.5)) # cv2.LINE_AA=16
cv2.destroyAllWindows()
cap.release() #'''
def box2oprectangle(box):
left, right, top, bottom = box
width = np.abs(right - left)
height = np.abs(top - bottom)
max_length = int(max(width, height))
center = (int(left + (width / 2)), int(bottom - (height / 2)))
new_top = (int(center[0] - max_length / 1.3),
int(center[1] - max_length / 1.3))
max_length = int(max_length * 1.6)
hand_rectangle = op.Rectangle(new_top[0], new_top[1], max_length,
max_length)
return hand_rectangle
def box2oprectangle(self, box):
[left, right, top, bottom] = box
width = np.abs(right - left)
height = np.abs(top - bottom)
max_length = int(max(width, height))
center = (int(left + (width / 2)), int(bottom - (height / 2)))
# Openpose hand detector needs the bounding box to be quite big , so we make it bigger
# Top point for rectangle
new_top = (int(center[0] - max_length / 1.3),
int(center[1] - max_length / 1.3))
max_length = int(max_length * 1.6)
hand_rectangle = op.Rectangle(new_top[0], new_top[1], max_length,
max_length)
return hand_rectangle
def detect_keypoints(image, opWrapper_, hand_boxes=None):
if hand_boxes is None:
hand_boxes = [[0, image.shape[1] - 1, 0, image.shape[0] - 1]]
# Left hand only
hands_rectangles = [[box2oprectangle(box),
op.Rectangle(0., 0., 0., 0.)] for box in hand_boxes]
datum = op.Datum()
datum.cvInputData = image
datum.handRectangles = hands_rectangles
opWrapper_.emplaceAndPop([datum])
if datum.handKeypoints[0].shape == ():
hand_keypoints = []
else:
hand_keypoints = datum.handKeypoints[0]
return hand_keypoints, datum.cvOutputData
if (depth_center!=0 and depth_center<1500) :
print("On est sense retourner ca")
print(depth_center)
else :
depth_center = 0
try:
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Read image and face rectangle locations
imageToProcess = cv2.imread('Coloredimage.jpg')
faceRectangles = [
op.Rectangle(face[0],face[1],face[2],face[3])
]
# Create new datum
datum = op.Datum()
datum.cvInputData = imageToProcess
datum.faceRectangles = faceRectangles
# Process and display image
opWrapper.emplaceAndPop([datum])
print("Face keypoints: \n" + str(datum.faceKeypoints))
cv2.imshow("OpenPose 1.4.0 - Tutorial Python API", datum.cvOutputData)
cv2.waitKey(0)
except Exception as e:
# print(e)
sys.exit(-1)
if key not in params: params[key] = next_item
# Construct it from system arguments
# op.init_argv(args[1])
# oppython = op.OpenposePython()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Read image and face rectangle locations
# If you use face_detector 2 and body 0 options, then use must provide the face regions
imageToProcess = cv2.imread(args[0].image_path)
faceRectangles = [
op.Rectangle(330.119385, 277.532715, 48.717274, 48.717274),
op.Rectangle(24.036991, 267.918793, 65.175171, 65.175171),
op.Rectangle(151.803436, 32.477852, 108.295761, 108.295761),
]
# Create new datum
datum = op.Datum()
datum.cvInputData = imageToProcess
datum.faceRectangles = faceRectangles
# Process and display image
opWrapper.emplaceAndPop(op.VectorDatum([datum]))
print("Face keypoints: \n" + str(datum.faceKeypoints))
cv2.imshow("OpenPose 1.7.0 - Tutorial Python API", datum.cvOutputData)
cv2.waitKey(0)
except Exception as e:
def hand_box(sz, left=0):
non = pyop.Rectangle(0, 0, 0, 0)
if type(sz) == cv2.VideoCapture: # wh
sz = max(sz.get(3), sz.get(4))
box = pyop.Rectangle(0, 0, sz, sz)
return [[box, non] if left else [non, box]]
if len(files) == 0:
raise Exception("Input directory is empty")
print("Found", len(files), "files")
images = [cv2.imread(os.path.join(args.input, f)) for f in files]
data = []
for image in images:
# Create new datum
datum = op.Datum()
datum.cvInputData = image
datum.handRectangles = [
# Left/Right hands person 0
[
op.Rectangle(0., 0., 0., 0.),
op.Rectangle(0, 0, image.shape[0], image.shape[1]),
],
]
data.append(datum)
get_hand_detector().emplaceAndPop([datum])
for f, image, datum in zip(files, images, data):
pose = [[float(x) for x in r][:2]
for r in np.array(datum.handKeypoints[1][0], dtype=np.float16)]
print(pose)
save_loc = os.path.join(args.output, f)
if args.format == "json":
json.dump(pose, open(save_loc + ".json", "w"))
else:
# op.init_argv(args[1])
# oppython = op.OpenposePython()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Read image and hand rectangle locations
# If you use hand_detector 2 and body 0 options, then use must provide the hand regions
imageToProcess = cv2.imread(args[0].image_path)
handRectangles = [
# Left/Right hands person 0
[
op.Rectangle(320.035889, 377.675049, 69.300949, 69.300949),
op.Rectangle(0., 0., 0., 0.),
],
# Left/Right hands person 1
[
op.Rectangle(80.155792, 407.673492, 80.812706, 80.812706),
op.Rectangle(46.449715, 404.559753, 98.898178, 98.898178),
],
# Left/Right hands person 2
[
op.Rectangle(185.692673, 303.112244, 157.587555, 157.587555),
op.Rectangle(88.984360, 268.866547, 117.818230, 117.818230),
]
]
# Create new datum
def Rectangle(*args, **kwargs):
return pyopenpose.Rectangle(*args, **kwargs)
while (True):
start = time.time()
rec, imageToProcess = cam.read()
if not rec:
break
# Read image and face rectangle locations
#imageToProcess = cv2.imread(args[0].image_path)
print(imageToProcess.shape)
height = imageToProcess.shape[0]
width = imageToProcess.shape[1]
handRectangles = [
# Left/Right hands person 0
[
op.Rectangle(0., 0., height, height),
op.Rectangle(width / 2., 0., height, height),
],
# Left/Right hands person 1
[
op.Rectangle(0., 0., height, height),
op.Rectangle(width / 2, 0., height, height),
]
]
# Create new datum
datum = op.Datum()
datum.cvInputData = imageToProcess
datum.handRectangles = handRectangles
# Process and display image
