def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def start_classify():
model = load_model()
print("Done LOADING!")
while True:
queue = db.lrange(settings.IMAGE_QUEUE, 0, settings.BATCH_SIZE - 1)
imageIDs = []
batch = None
for q in queue:
q = json.loads(q.decode("utf-8"))
#print(type(q["image"]))
image = base64_decode_image(q["image"], settings.IMAGE_DTYPE)
if batch is None:
batch = image
else:
batch = np.vstack([batch, image])
imageIDs.append(q["id"])
if len(imageIDs) > 0:
print("* Batch size: {}".format(batch.shape))
results = [im_detect(model, image)]
for (imageID, ans) in zip(imageIDs, results):
#print(ans)
output = []
scores = ans[0]
boxes = ans[1]
r = {"labels": scores.tolist(), "boxes": boxes.tolist()}
output.append(r)
db.set(imageID, json.dumps(output))
db.ltrim(settings.IMAGE_QUEUE, len(imageIDs), -1)
time.sleep(settings.SERVER_SLEEP)
def demo(sess, net, image_name, path_list):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im = cv2.imdecode(np.fromfile(image_name, dtype=np.uint8), -1)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
#print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.5
NMS_THRESH = 0.3
result = np.zeros((46, 147,3), dtype=np.uint8)
outout = np.zeros((224, 224,3), dtype=np.uint8)
color = 'blue plate'
for cls_ind, cls in enumerate(CLASSES[1:]):
# print(cls_ind, cls)
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
#print(cls_boxes)
# print('scores',scores)
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
# print(dets)
result,outout,color = vis_detections(im, cls, dets, path_list, result,outout,color,thresh=CONF_THRESH)
return result,outout,color
cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, i)
while (cap.isOpened()):
ret, frame = cap.read()
if i % 5 == 0:
cv2.setTrackbarPos('frame', 'stair detection', int(cap.get(1)))
"""Detect object classes in an image using pre-computed object proposals."""
copy = frame.copy()
frame = cv2.resize(copy, (640, 480))
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, frame)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)