def image_enqueue(image_path):
start_time = time.time()
with open(image_path, "rb") as imageFile:
# generate an ID for the classification then add the
# classification ID + image to the queue
k = str(uuid.uuid4())
image = helpers.base64_encode_image(imageFile.read())
d = {"id": k, "path": image_path, "image": image}
DB.rpush(settings.IMAGE_QUEUE, json.dumps(d))
print("Push to redis %d ms" % int(round((time.time() - start_time) * 1000)))
def image_enqueue(fname, img, label=None):
'''
:param fname: cutted image name, e.g. 0_320.jpg
:param img: numpy ndarray
:param label: region label corresponded
:return: none
'''
start_time = time.time()
k = str(uuid.uuid4())
img_encoded = helpers.base64_encode_image(img)
d = {"id": str(k), "path": fname, "image": img_encoded}
DB.xadd(settings.IMAGE_STREAMING, d)
def streaming():
cam = cv2.VideoCapture(0) #(settings.RTSP_ADDR)
start = time.time()
while cam.isOpened():
_, image = cam.read()
cv2.imshow('{}-{}'.format(image.shape[0], image.shape[1]), image)
if cv2.waitKey(1) == ord('q'):
exit()
if time.time() - start < 1:
continue
else:
start = time.time()
img_height, img_width, _ = image.shape
image = cv2.resize(image, (640, 480), interpolation=cv2.INTER_LINEAR)
image = image.astype(np.float32)
# generate an ID for the classification then add the
# classification ID + image to the queue
k = time.strftime('%Y-%m-%d %H:%M:%S') #str(uuid.uuid4())
image = base64_encode_image(image)
d = {"id": k, "image": image}
db.rpush(settings.IMAGE_QUEUE, json.dumps(d))
print(time.time() - start)
def add_embeds(self, embeds, labels):
base64_embeds = [base64_encode_image(e) for e in embeds]
self.db.rpush(EMBED_QUEUE, *base64_embeds)
self.db.rpush(LABEL_QUEUE, *labels)
self.__write_logs("INSERT", "{} embeds of {}".format(len(embeds), labels[0]))
return self.update_reload_status(value=True)
def push_image(self, id, image):
image = base64_encode_image(image)
d = {"id": id, "image": image}
return self.db.rpush(self.settings.IMAGE_QUEUE, json.dumps(d))