class recognitionGui:
def __init__(self, video_name):
self.title_window = "Demonstrateur reconnaissance de personne"
self.skeletonService = Skeleton()
self.cap = cv2.VideoCapture(video_name)
self.opWrapper = op.WrapperPython()
self.opWrapper.configure(params)
self.opWrapper.start()
self.gaitClassifier = GaitClassifier()
self.last_frame = None
self.kp_frames = []
self.stop = False
w, h = (int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
self.out = cv2.VideoWriter(
"Recording_" + datetime.datetime.now().strftime("%Y%m%d_%H%M%S") +
".mp4", cv2.VideoWriter_fourcc(*'MP4V'), 25, (w, h))
def run(self):
while (self.cap.isOpened() and not self.stop):
self.putFrame()
cv2.imshow(self.title_window, self.last_frame)
self.out.write(self.last_frame)
self.kp_frames = np.asarray(self.kp_frames)
print(self.kp_frames.shape)
if self.kp_frames.shape[0] >= 140:
for i in range(3):
mean = np.mean(self.kp_frames[:, i::3], axis=0)
std = np.std(self.kp_frames[:, i::3], axis=0)
self.kp_frames[:, i::3] = np.divide(self.kp_frames[:, i::3] -
mean,
std,
where=(std != 0))
pred, pred_value = self.gaitClassifier.demo(self.kp_frames)
pred = Gait(pred).name
else:
pred = "Unknown"
pred_value = None
self.last_frame = self.putPhoto("people/" + pred + ".png",
self.last_frame)
self.last_frame = self.putName(pred, self.last_frame)
if pred_value != None:
self.last_frame = self.putName(str(int(pred_value * 10000) / 100) +
" %",
self.last_frame,
offset=15)
cv2.imshow(self.title_window, self.last_frame)
for i in range(25):
self.out.write(self.last_frame)
self.out.release()
cv2.waitKey(0)
def putFrame(self):
ret, frame = self.cap.read()
if ret:
try:
# Process Image
datum = op.Datum()
datum.cvInputData = frame
self.opWrapper.emplaceAndPop([datum])
except:
pass
kp = datum.poseKeypoints
if not len(kp.shape) == 0:
self.kp_frames.append(kp[0].flatten())
frame = self.putSkeleton(kp, frame)
frame = self.putPhoto("people/Unknown.png", frame)
self.last_frame = np.array(frame)
frame = self.putName(None, frame)
cv2.imshow(self.title_window, frame)
self.out.write(frame)
key = cv2.waitKey(1)
if key == ord('q'):
self.stop = True
else:
self.cap.release()
def putSkeleton(self, kp, frame):
img = np.zeros(frame.shape, np.uint8)
img += 180
if not len(kp.shape) == 0:
window_size = [frame.shape[0], frame.shape[1], 50, 90]
img = self.skeletonService.drawSkeleton(kp, window_size, img)
img = cv2.resize(img, (int(
int(1 / 3 * frame.shape[0]) * 4 / 3), int(1 / 3 * frame.shape[0])))
img_w, img_h, _ = img.shape
frame[frame.shape[0] - img_w - 10:frame.shape[0] - 10,
frame.shape[1] - img_h - 10:frame.shape[1] - 10] = img
return frame
def putPhoto(self, path, frame):
photo = cv2.imread(path)
photo = cv2.resize(
photo, (int(1 / 3 * frame.shape[0]), int(1 / 3 * frame.shape[0])))
photo_w, photo_h, _ = photo.shape
frame[10:photo_w + 10, 10:photo_h + 10] = photo
return frame
def putName(self, name, frame, offset=0):
if name == None:
name = "Reconnaissance en cours ..."
font_scale = .3
else:
font_scale = .6 if offset == 0 else 0.45
font = cv2.FONT_HERSHEY_SIMPLEX
(text_width, text_height) = cv2.getTextSize(name,
font,
fontScale=font_scale,
thickness=1)[0]
img = frame.copy()
img = cv2.rectangle(
img, (10, int(offset + 11 + text_height + 1 / 3 * frame.shape[0])),
(int(10 + 1 / 3 * frame.shape[0]),
int(offset + 10 + 1 / 3 * frame.shape[0])), (255, 255, 255),
cv2.FILLED)
frame = cv2.addWeighted(img, .5, frame, .5, 0)
frame = cv2.putText(
frame, name,
(int(10 + 1 / 6 * frame.shape[0] - text_width / 2),
int(offset + 15 + text_height / 2 + 1 / 3 * frame.shape[0])),
font, font_scale, (20, 30, 0), 1, cv2.LINE_AA)
return frame
try:
# Process Image
datum = op.Datum()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
except:
break
#Get keypoints
kp = datum.poseKeypoints
if args["mode"] == "skeleton":
frame = np.zeros(frame.shape, np.uint8)
frame += 200
try:
frame = skeleton.drawSkeleton(kp, (x, y, w, h), frame)
except:
pass
else:
#Add skeleton
if viewSkeleton:
frame = datum.cvOutputData
#Normalize keypoints and predict pose
if args["mode"] != "openpose":
try:
frame = service.displayPrediction(frame, kp)
if args["mode"] == "pose" and isInDevMode and isSavingData:
dev_data = service.getData()
for data in dev_data:
