def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='Path of the detection model.',
required=True)
parser.add_argument('--label', help='Path of the labels file.')
parser.add_argument(
'--mode',
help='Mode for de detection: OBJECT_DETECTION or IMAGE_CLASSIFICATION',
required=True)
parser.add_argument('--camera',
help='Camera source (if multiple available)',
type=int,
required=False)
args = parser.parse_args()
# Initialize engine.
if args.mode == "OBJECT_DETECTION":
engine = DetectionEngine(args.model)
elif args.mode == "IMAGE_CLASSIFICATION":
engine = ClassificationEngine(args.model)
else:
print(
"Please insert the mode from OBJECT_DETECTION or IMAGE_CLASSIFICATION"
)
exit()
labels = read_label_file(args.label) if args.label else None
label = None
camera = args.camera if args.camera else 0
# Initialize the camera
cam = cv2.VideoCapture(camera)
# Initialize the timer for fps
start_time = time.time()
frame_times = deque(maxlen=40)
while True:
ret, cv2_im = cam.read()
#we are transforming the npimage to img, and the TPU library/utils are doing the
#inverse process
#The CV2 Way
#pil_im = Image.fromarray(cv2.cvtColor(cv2_im,cv2.COLOR_BGR2RGB))
pil_im = Image.fromarray(np.uint8(cv2_im)).convert('RGB')
#This is the tf utils way for the transformation. It needs numpy, and is slightly slower
if args.mode == "OBJECT_DETECTION":
ans = engine.DetectWithImage(pil_im,
threshold=0.05,
keep_aspect_ratio=True,
relative_coord=False,
top_k=10)
if ans:
print("{} object(s) detected".format(len(ans)))
for obj in ans:
if obj.score > 0.4:
if labels:
label = labels[obj.label_id]
if SHOW_CONFIDENCE_IN_LABEL:
label = label + "({0:.2f})".format(obj.score)
draw_rectangles(obj.bounding_box, cv2_im, label=label)
else:
draw_text(cv2_im, 'No object detected!')
else:
i = 0
for result in engine.ClassifyWithImage(pil_im, top_k=5):
if result:
label = labels[result[0]]
score = result[1]
draw_text(cv2_im, label, i)
i += 1
else:
draw_text(cv2_im, 'No classification detected!')
lastInferenceTime = engine.get_inference_time()
frame_times.append(time.time())
fps = len(frame_times) / float(frame_times[-1] - frame_times[0] +
0.001)
draw_text(cv2_im, "{:.1f} / {:.2f}ms".format(fps, lastInferenceTime))
# flipping the image:
#cv2.flip(cv2_im, 1)
#resizing the image
#cv2_im = cv2.resize(cv2_im, (800, 600))
cv2.imshow('object detection', cv2_im)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
#end
exit()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='Path of the detection model.',
required=True)
parser.add_argument('--label', help='Path of the labels file.')
parser.add_argument(
'--mode',
help='Mode for de detection: OBJECT_DETECTION or IMAGE_CLASSIFICATION',
required=True)
parser.add_argument('--camera',
help='Camera source (if multiple available)',
type=int,
required=False)
args = parser.parse_args()
# Initialize engine.
if args.mode == "OBJECT_DETECTION":
engine = DetectionEngine(args.model)
elif args.mode == "IMAGE_CLASSIFICATION":
engine = ClassificationEngine(args.model)
else:
print(
"Please insert the mode from OBJECT_DETECTION or IMAGE_CLASSIFICATION"
)
exit()
labels = read_label_file(args.label) if args.label else None
label = None
camera = args.camera if args.camera else 0
# Initialize the camera
#cam = cv2.VideoCapture(camera)
camera = PiCamera()
time.sleep(2)
camera.resolution = (640, 480)
# Create the in-memory stream
stream = io.BytesIO()
# Initialize the timer for fps
start_time = time.time()
frame_times = deque(maxlen=40)
while True:
#ret, cv2_im = cam.read()
stream = io.BytesIO() #wipe the contents
camera.capture(stream, format='jpeg', use_video_port=True)
stream.seek(0)
pil_im = Image.open(stream)
cv2_im = np.array(pil_im)
cv2_im = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)
if args.mode == "OBJECT_DETECTION":
ans = engine.DetectWithImage(pil_im,
threshold=0.05,
keep_aspect_ratio=True,
relative_coord=False,
top_k=10)
if ans:
for obj in ans:
if obj.score > 0.4:
if labels:
label = labels[obj.label_id] + " - {0:.2f}".format(
obj.score)
draw_rectangles(obj.bounding_box, cv2_im, label=label)
else:
draw_text(cv2_im, 'No object detected!')
else:
i = 0
for result in engine.ClassifyWithImage(pil_im, top_k=5):
if result:
label = labels[result[0]]
score = result[1]
draw_text(cv2_im, label, i)
i += 1
else:
draw_text(cv2_im, 'No classification detected!')
lastInferenceTime = engine.get_inference_time()
frame_times.append(time.time())
fps = len(frame_times) / float(frame_times[-1] - frame_times[0] +
0.001)
draw_text(cv2_im, "{:.1f} / {:.2f}ms".format(fps, lastInferenceTime))
#print("FPS / Inference time: " + "{:.1f} / {:.2f}ms".format(fps, lastInferenceTime))
#flipping the image: cv2.flip(cv2_im, 1)
#cv2_im = cv2.resize(cv2_im, (800, 600))
cv2.imshow('object detection', cv2_im)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
exit()
break
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='Path of the detection model.',
required=True)
parser.add_argument('--label', help='Path of the labels file.')
parser.add_argument(
'--mode',
help='Mode for de detection: OBJECT_DETECTION or IMAGE_CLASSIFICATION',
required=True)
parser.add_argument('--camera',
help='Camera source (if multiple available)',
type=int,
required=False)
args = parser.parse_args()
# Initialize engine.
if args.mode == "OBJECT_DETECTION":
engine = DetectionEngine(args.model)
elif args.mode == "IMAGE_CLASSIFICATION":
engine = ClassificationEngine(args.model)
else:
print(
"Please insert the mode from OBJECT_DETECTION or IMAGE_CLASSIFICATION"
)
exit()
labels = read_label_file(args.label) if args.label else None
label = None
camera = args.camera if args.camera else 0
# Initialize the camera
cam = cv2.VideoCapture(camera)
# Initialize the screen
BLACK = (0, 0, 0)
WIDTH = 800
HEIGHT = 600
screen = pygame.display.set_mode((WIDTH, HEIGHT), 0, 32)
screen.fill(BLACK)
pygame.init()
# Initialize the timer for fps
start_time = time.time()
frame_times = deque(maxlen=40)
while True:
ret, cv2_im = cam.read()
#we are transforming the npimage to img, and the TPU library/utils are doing the
#inverse process
cv2_im = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)
pil_im = Image.fromarray(cv2_im)
#pil_im = Image.fromarray(np.uint8(cv2_im)).convert('RGB')
#This is the tf utils way for the transformation. It needs numpy
if args.mode == "OBJECT_DETECTION":
ans = engine.DetectWithImage(pil_im,
threshold=0.05,
keep_aspect_ratio=True,
relative_coord=False,
top_k=10)
if ans:
print("{} object(s) detected".format(len(ans)))
for obj in ans:
if obj.score > 0.5:
if labels:
label = labels[obj.label_id] + " - {0:.2f}".format(
obj.score)
draw_rectangles(obj.bounding_box, cv2_im, label=label)
else:
draw_text(cv2_im, 'No object detected!')
else:
i = 0
for result in engine.ClassifyWithImage(pil_im, top_k=5):
if result:
label = labels[result[0]]
score = result[1]
draw_text(cv2_im, label, i)
i += 1
else:
draw_text(cv2_im, 'No classification detected!')
frame_times.append(time.time())
fps = len(frame_times) / float(frame_times[-1] - frame_times[0] +
0.001)
draw_text(cv2_im, "{:.1f}".format(fps))
#flipping the image: cv2.flip(cv2_im, 1) # Just needed if you are using a webcam as a mirror
#cv2_im = cv2.resize(cv2_im, (800, 600))
#put back the image into the screen using a pygame image
pygameimage = pygame.image.frombuffer(cv2_im.tostring(),
cv2_im.shape[1::-1], "RGB")
screen.blit(pygameimage, (0, 0))
pygame.display.update()
#end
cv2.VideoCapture.release(cam)
