def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model',
help='File path of .tflite file.',
required=True)
parser.add_argument('--labels',
help='File path of labels file.',
required=True)
parser.add_argument('--threshold',
help='Score threshold for detected objects.',
required=False,
type=float,
default=0.4)
args = parser.parse_args()
labels = load_labels(args.labels)
interpreter = Interpreter(
args.model,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')]) #coral
interpreter.allocate_tensors()
_, input_height, input_width, _ = interpreter.get_input_details(
)[0]['shape']
# initialize variables to calculate FPS
instantaneous_frame_rates = []
# initialize variable for tracker use
#trackers = []
#j=0
counter = 0
t = None
#win = dlib.image_window()
test_start_time = time.monotonic()
with picamera.PiCamera(resolution=(CAMERA_WIDTH, CAMERA_HEIGHT),
framerate=10) as camera:
camera.start_preview() #alpha = 200
try:
stream = io.BytesIO()
annotator = Annotator(camera)
for _ in camera.capture_continuous(stream,
format='jpeg',
use_video_port=True):
#start_time = time.monotonic()
#print("Test FPS: " + str(1/(time.monotonic() - test_start_time)))
#test_start_time = time.monotonic()
stream.seek(0)
counter += 1
#start_time = time.monotonic() #start_time declaration moved to give a more accurate measurement to calculate FPS
test_start_time = time.monotonic()
image = Image.open(stream).convert('RGB')
dlib_img = np.asarray(image)
print("Test FPS: " + str(1 /
(time.monotonic() - test_start_time)))
#image.save("test_save.jpg")
#image = Image.open(stream).convert('RGB').resize((input_width, input_height), Image.ANTIALIAS)
#image = image.resize((input_width, input_height), Image.ANTIALIAS)
#dlib_img = dlib.load_rgb_image("/home/pi/Desktop/object_detection/object_detection_tpu_tracking_dlib/test_save.jpg")
annotator.clear()
# if there are no trackes, first must try to detect objects
#if len(trackers) == 0:
if counter < 10:
#dlib_img = np.asarray(image)
image = image.resize((input_width, input_height),
Image.ANTIALIAS)
results = detect_objects(interpreter, image,
args.threshold)
# get the coordinates for all bounding boxes within frame
rects = get_rects(results)
for i in np.arange(0, len(results)):
#format bounding box coordinates
box = np.array(rects[i])
(startY, startX, endY, endX) = box.astype("int")
print(startX, startY, endX, endY)
#x = (startX + endX) / 2
#y = (startY + endY) / 2
dlib_rect = dlib.rectangle(startX, startY, endX, endY)
t = dlib.correlation_tracker()
print("setting")
t.start_track(dlib_img, dlib_rect)
#trackers.append(t)
#annotator.clear()
#annotator.centroid(x, y)
annotate_objects(annotator, results, labels)
else:
t.update(dlib_img)
pos = t.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
x = (startX + endX) / 2
y = (startY + endY) / 2
#annotator.centroid(x, y)
#annotator.clear()
annotator.bounding_box([startX, startY, endX, endY])
#if (counter % 20) == 0:
#t = None
#annotator.clear()
annotator.update()
stream.seek(0)
stream.truncate()
#print(time.monotonic())
finally:
camera.stop_preview()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model',
help='File path of .tflite file.',
required=True)
parser.add_argument('--labels',
help='File path of labels file.',
required=True)
parser.add_argument('--threshold',
help='Score threshold for detected objects.',
required=False,
type=float,
default=0.4)
args = parser.parse_args()
labels = load_labels(args.labels)
interpreter = Interpreter(
args.model,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')]) #coral
interpreter.allocate_tensors()
_, input_height, input_width, _ = interpreter.get_input_details(
)[0]['shape']
# initialize variables to calculate FPS
instantaneous_frame_rates = []
# initialize variable for tracker use
t = None
test_time_all = []
counter = 0
test_start_time = time.monotonic()
with picamera.PiCamera(resolution=(CAMERA_WIDTH, CAMERA_HEIGHT),
framerate=30) as camera:
camera.start_preview() #alpha = 200
start_time = time.monotonic()
try:
stream = io.BytesIO()
annotator = Annotator(camera)
for _ in camera.capture_continuous(stream,
format='jpeg',
use_video_port=True):
test_time = (time.monotonic() - test_start_time)
test_time_all.append(test_time)
print(
str(sum(test_time_all) / len(test_time_all)) + ", FPS: " +
str(1 / (sum(test_time_all) / len(test_time_all))))
stream.seek(0)
counter += 1
image = Image.open(stream).convert('RGB')
cv_img = np.asarray(image)
annotator.clear()
# if there are no trackes, first must try to detect objects
if t == None:
image = image.resize((input_width, input_height),
Image.ANTIALIAS)
results = detect_objects(interpreter, image,
args.threshold)
rects = get_rects(results)
for i in np.arange(0, len(results)):
#format bounding box coordinates
print("new tracker")
box = np.array(rects[i])
(startY, startX, endY, endX) = box.astype("int")
cv_rect = (startX, startY, endX - startX,
endY - startY)
t = cv2.TrackerMOSSE_create()
t.init(cv_img, cv_rect)
annotator.bounding_box([startX, startY, endX, endY])
#annotate_objects(annotator, results, labels)
else:
(success, box) = t.update(cv_img)
if success:
annotator.bounding_box(
[box[0], box[1], box[0] + box[2], box[1] + box[3]])
#cv2.rectangle(cv_img, (int(box[0]), int(box[1])), (int(box[0] + box[2]), int(box [1] + box[3])),(0, 255, 0), 2)
#if (counter % 40) == 0:
#t = None
#elapsed_ms = (time.monotonic() - start_time) * 1000
#annotator.text([5, 0], '%.1f ms' % (elapsed_ms))
#frame_rate = 1/ ((time.monotonic() - start_time))
#start_time = time.monotonic()
#print(frame_rate)
#calculate average FPS
#instantaneous_frame_rates.append(frame_rate)
#avg_frame_rate = sum(instantaneous_frame_rates)/len(instantaneous_frame_rates)
#print("FPS: " + str(avg_frame_rate))
#annotator.text([5, 15], '%.1f FPS' % (avg_frame_rate))
#annotator.clear()
annotator.update()
stream.seek(0)
stream.truncate()
test_start_time = time.monotonic()
finally:
camera.stop_preview()