def show_webcam(address):
cam = webcam.WebcamStream(address)
cam.start_stream_threads()
ssd = SSD()
global i2name
i2name = pickle.load(open("i2name.p", "rb"))
cv2.namedWindow("outputs", cv2.WINDOW_NORMAL)
boxes_ = None
confidences_ = None
while True:
sample = cam.image
resized_img = skimage.transform.resize(sample,
(image_size, image_size))
pred_labels_f, pred_locs_f = ssd.sess.run(
[ssd.pred_labels, ssd.pred_locs],
feed_dict={
ssd.imgs_ph: [resized_img],
ssd.bn: False
})
boxes_, confidences_ = matcher.format_output(pred_labels_f[0],
pred_locs_f[0], boxes_,
confidences_)
resize_boxes(resized_img, sample, boxes_)
draw_outputs(np.asarray(sample) / 255.0, boxes_, confidences_, wait=10)
def single_image(self, sample, min_conf=0.01, nms=0.45):
resized_img = cv2.resize(sample, (image_size, image_size))
pred_labels_f, pred_locs_f, step = self.sess.run([self.pred_labels, self.pred_locs, self.global_step], feed_dict={self.imgs_ph: [resized_img], self.bn: False})
boxes_, confidences_ = matcher.format_output(pred_labels_f[0], pred_locs_f[0])
resize_boxes(resized_img, sample, boxes_, scale=float(image_size))
return postprocess_boxes(boxes_, confidences_, min_conf, nms)
def evaluate_images():
tb = TB()
cv2.namedWindow("outputs", cv2.WINDOW_NORMAL)
test_loader = sLoader.SVT('./svt1/train.xml', './svt1/test.xml')
while True:
imgs, anns = test_loader.nextBatch(3,'test')
pred_labels_f, pred_locs_f, step = tb.sess.run([tb.pred_labels, tb.pred_locs, tb.global_step],
feed_dict={tb.imgs_ph: imgs, tb.bn: False})
boxes_, confidences_ = matcher.format_output(pred_labels_f[0], pred_locs_f[0])
draw_outputs(imgs[0], boxes_, confidences_, wait=0)
def match_boxes(batch_i):
#a = time.time()
matches = box_matcher.match_boxes(pred_labels_f[batch_i], anns[batch_i])
#print("a: %f" % (time.time() - a))
#a = time.time()
positives_f, negatives_f, true_labels_f, true_locs_f = prepare_feed(matches)
batch_values[batch_i] = (positives_f, negatives_f, true_labels_f, true_locs_f)
if batch_i == 0:
boxes_, confidences_ = matcher.format_output(pred_labels_f[batch_i], pred_locs_f[batch_i])
if FLAGS.display:
draw_outputs(imgs[batch_i], boxes_, confidences_)
draw_matches(imgs[batch_i], c.defaults, matches, anns[batch_i])
draw_matches2(imgs[batch_i], positives_f, negatives_f, true_labels_f, true_locs_f)
def evaluate_images():
ssd = SSD()
cv2.namedWindow("outputs", cv2.WINDOW_NORMAL)
loader = coco.Loader(False)
test_batches = loader.create_batches(1, shuffle=True)
global i2name
i2name = loader.i2name
while True:
batch = test_batches.next()
imgs, anns = loader.preprocess_batch(batch)
pred_labels_f, pred_locs_f, step = ssd.sess.run(
[ssd.pred_labels, ssd.pred_locs, ssd.global_step],
feed_dict={
ssd.imgs_ph: imgs,
ssd.bn: False
})
boxes_, confidences_ = matcher.format_output(pred_labels_f[0],
pred_locs_f[0])
draw_outputs(imgs[0], boxes_, confidences_, wait=0)