def start_video(self, model):
camera = cv2.VideoCapture(0)
while True:
frame = camera.read()[1]
if frame is None:
continue
image_array = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_array = cv2.resize(image_array, (300, 300))
image_array = substract_mean(image_array)
image_array = np.expand_dims(image_array, 0)
predictions = model.predict(image_array)
detections = detect(predictions, self.prior_boxes)
plot_detections(detections, frame, 0.6, self.arg_to_class,
self.colors)
cv2.imshow('webcam', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
def test_net(save_folder,
net,
cuda,
dataset,
transform,
top_k,
im_size=300,
thresh=0.05):
"""Test a Fast R-CNN network on an image database."""
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap) + 1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('ssd300_120000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
# detect = Detect(21, 0, 200, 0.01, .45)
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
keras_image = np.squeeze(im)
# keras_image = substract_mean(x)
keras_image_input = np.expand_dims(keras_image, axis=0)
keras_output = net.predict(keras_image_input)
detections = detect(keras_output, prior_boxes)
# detections = detect.forward(keras_output, prior_boxes)
_t['im_detect'].tic()
# detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
detection_size = 21
# for j in range(1, detections.size(1)):
for j in range(1, detection_size):
dets = detections[0, j, :]
mask = np.squeeze(dets[:, 0] > 0.01)
dets = dets[mask]
if len(dets) == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
# scores = dets[:, 0].cpu().numpy()
scores = dets[:, 0]
cls_dets = np.hstack(
(boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1, num_images,
detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)