def detect_image(img_file):
if not os.path.exists(img_file):
print('can not find image: ', img_file)
img = Image.open(img_file)
img = ImageOps.fit(img, [data_shape, data_shape], Image.ANTIALIAS)
print(img)
origin_img = np.array(img)
img = origin_img - np.array([123, 117, 104])
# organize as [batch-channel-height-width]
img = np.transpose(img, (2, 0, 1))
img = img[np.newaxis, :]
# convert to ndarray
img = nd.array(img)
print('input image shape: ', img.shape)
net = ToySSD(num_class)
ctx = mx.cpu()
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
net.collect_params().reset_ctx(ctx)
params = 'ssd_pretrained.params'
net.load_params(params, ctx=ctx)
anchors, cls_preds, box_preds = net(img.as_in_context(ctx))
print('anchors', anchors)
print('class predictions', cls_preds)
print('box delta predictions', box_preds)
# convert predictions to probabilities using softmax
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0, 2, 1)), mode='channel')
# apply shifts to anchors boxes, non-maximum-suppression, etc...
output = MultiBoxDetection(*[cls_probs, box_preds, anchors], force_suppress=True, clip=False)
output = output.asnumpy()
print(output)
print(output.shape)
pens = dict()
plt.imshow(origin_img)
thresh = 0.69
for det in output[0]:
cid = int(det[0])
if cid < 0:
continue
score = det[1]
if score < thresh:
continue
if cid not in pens:
pens[cid] = (random.random(), random.random(), random.random())
scales = [origin_img.shape[1], origin_img.shape[0]] * 2
xmin, ymin, xmax, ymax = [int(p * s) for p, s in zip(det[2:6].tolist(), scales)]
rect = plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, edgecolor=pens[cid], linewidth=3)
plt.gca().add_patch(rect)
text = class_names[cid]
plt.gca().text(xmin, ymin - 2, '{:s} {:.3f}'.format(text, score),
bbox=dict(facecolor=pens[cid], alpha=0.5),
fontsize=12, color='white')
plt.axis('off')
plt.savefig('result.png', dpi=100)
plt.show()
def predict_bounding_boxes(net,
image,
min_c,
overlap_thres,
topk,
ctx=mx.gpu()):
'''
Given the outputs of the dataset (image and bounding box) and the network,
the predicted bounding boxes are provided.
Parameters
----------
net: SSD
The trained SSD network.
image: np.array
A grayscale image of the handwriting passages.
Returns
-------
predicted_bb: [(x, y, w, h)]
The predicted bounding boxes.
'''
image = mx.nd.array(image).expand_dims(axis=2)
image = mx.image.resize_short(image, 350)
image = image.transpose([2, 0, 1]) / 255.
image = image.as_in_context(ctx)
image = image.expand_dims(0)
bb = np.zeros(shape=(13, 5))
bb = mx.nd.array(bb)
bb = bb.as_in_context(ctx)
bb = bb.expand_dims(axis=0)
default_anchors, class_predictions, box_predictions = net(image)
box_target, box_mask, cls_target = net.training_targets(
default_anchors, class_predictions, bb)
cls_probs = mx.nd.SoftmaxActivation(mx.nd.transpose(
class_predictions, (0, 2, 1)),
mode='channel')
predicted_bb = MultiBoxDetection(
*[cls_probs, box_predictions, default_anchors],
force_suppress=True,
clip=False)
predicted_bb = box_nms(predicted_bb,
overlap_thresh=overlap_thres,
valid_thresh=min_c,
topk=topk)
predicted_bb = predicted_bb.asnumpy()
predicted_bb = predicted_bb[0, predicted_bb[0, :, 0] != -1]
predicted_bb = predicted_bb[:, 2:]
predicted_bb[:, 2] = predicted_bb[:, 2] - predicted_bb[:, 0]
predicted_bb[:, 3] = predicted_bb[:, 3] - predicted_bb[:, 1]
return predicted_bb
def predict_bounding_boxes(net, image, bb):
'''
Given the outputs of the dataset (image and bounding box) and the network,
the predicted bounding boxes are provided.
Parameters
----------
net: SSD
The trained SSD network.
image: np.array
A grayscale image of the handwriting passages.
bb: [(x1, y1, x2, y2)]
A tuple that contains the bounding box.
Returns
-------
predicted_bb: [(x, y, w, h)]
The predicted bounding boxes.
actual_bb: [(x, y, w, h)]
The actual bounding bounding boxes.
'''
image, bb = transform(image, bb)
image = image.as_in_context(ctx[0])
image = image.expand_dims(axis=0)
bb = bb.as_in_context(ctx[0])
bb = bb.expand_dims(axis=0)
default_anchors, class_predictions, box_predictions = net(image)
box_target, box_mask, cls_target = net.training_targets(
default_anchors, class_predictions, bb)
cls_probs = nd.SoftmaxActivation(nd.transpose(class_predictions,
(0, 2, 1)),
mode='channel')
predicted_bb = MultiBoxDetection(
*[cls_probs, box_predictions, default_anchors],
force_suppress=True,
clip=False)
predicted_bb = box_nms(predicted_bb,
overlap_thresh=overlap_thres,
valid_thresh=min_c,
topk=topk)
predicted_bb = predicted_bb.asnumpy()
predicted_bb = predicted_bb[0, predicted_bb[0, :, 0] != -1]
predicted_bb = predicted_bb[:, 2:]
predicted_bb[:, 2] = predicted_bb[:, 2] - predicted_bb[:, 0]
predicted_bb[:, 3] = predicted_bb[:, 3] - predicted_bb[:, 1]
labeled_bb = bb[:, :, 1:].asnumpy()
labeled_bb[:, :, 2] = labeled_bb[:, :, 2] - labeled_bb[:, :, 0]
labeled_bb[:, :, 3] = labeled_bb[:, :, 3] - labeled_bb[:, :, 1]
labeled_bb = labeled_bb[0]
return predicted_bb, labeled_bb
def predict(x, net, ctx):
anchors, cls_preds, box_preds = net(x.as_in_context(ctx))
cls_probs = nd.SoftmaxActivation(
cls_preds.transpose((0, 2, 1)), mode='channel')
return MultiBoxDetection(cls_probs, box_preds, anchors, force_suppress=True, clip=False)
'''
def test(net,ctx):#,image):
#image = cv2.imread('/BELLO/dog/data/normalsize/n02091244_3075.jpg')
image = cv2.imread('/BELLO/dog/data/dog3.jpg')
x = preprocess(image)
anchors, cls_preds, box_preds = net(x.as_in_context(ctx))
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0, 2, 1)), mode='channel')
output = MultiBoxDetection(*[cls_probs, box_preds, anchors], force_suppress=True, clip=False)
#print(output[0].asnumpy())
display(image[:, :, (2, 1, 0)], output[0].asnumpy(), thresh=0.7)
def inference(x, epochs= 295):
ctx = mx.cpu(1)
net = ToySSD(1)
start_time = time.time()
net.load_params('models/ssd_%d.params' % epochs, ctx)
anchors, cls_preds, box_preds = net(x.as_in_context(ctx))
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0,2,1)), mode = 'channel')
output = MultiBoxDetection(*[cls_probs, box_preds, anchors], force_suppress = False, clip = False, nms_threshold = 0.001 )
end_time = time.time()
print(end_time-start_time)
return output
def forward(img_path, net):
ctx = mx.gpu(1)
img_original = cv2.imread(img_path)
img = preprocess(img_original)
anchors, cls_preds, box_preds = net(img.as_in_context(ctx))
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0, 2, 1)),
mode='channel')
output = MultiBoxDetection(*[cls_probs, box_preds, anchors],
force_suppress=True,
clip=True,
nms_threshold=0.01)
return img_original, output
def inference(x, epochs=295):
ctx = mx.cpu(1)
net = ToySSD(1)
net.load_params('models/ssd_%d.params' % epochs, ctx)
print("load sucecuss")
anchors, cls_preds, box_preds = net(x.as_in_context(ctx))
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0, 2, 1)),
mode='channel')
output = MultiBoxDetection(*[cls_probs, box_preds, anchors],
force_suppress=True,
clip=False)
return output
image = cv2.imread('img/pikachu.jpg')
x = preprocess(image)
print('x', x.shape)
# if pre-trained model is provided, we can load it
# net.load_params('ssd_%d.params' % epochs, ctx)
anchors, cls_preds, box_preds = net(x.as_in_context(ctx))
print('anchors', anchors)
print('class predictions', cls_preds)
print('box delta predictions', box_preds)
from mxnet.contrib.ndarray import MultiBoxDetection
# convert predictions to probabilities using softmax
cls_probs = nd.SoftmaxActivation(nd.transpose(cls_preds, (0, 2, 1)), mode='channel')
# apply shifts to anchors boxes, non-maximum-suppression, etc...
output = MultiBoxDetection(*[cls_probs, box_preds, anchors], force_suppress=True, clip=False)
print(output)
def display(img, out, thresh=0.5):
import random
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (10,10)
pens = dict()
plt.clf()
plt.imshow(img)
for det in out:
cid = int(det[0])
if cid < 0:
continue
score = det[1]
def generate_output_image(box_predictions, default_anchors, cls_probs,
box_target, box_mask, cls_target, x, y):
'''
Generate the image with the predicted and actual bounding boxes.
Parameters
----------
box_predictions: nd.array
Bounding box predictions relative to the anchor boxes, output of the network
default_anchors: nd.array
Anchors used, output of the network
cls_probs: nd.array
Output of nd.SoftmaxActivation(nd.transpose(class_predictions, (0, 2, 1)), mode='channel')
where class_predictions is the output of the network.
box_target: nd.array
Output classification probabilities from network.training_targets(default_anchors, class_predictions, y)
box_mask: nd.array
Output bounding box predictions from network.training_targets(default_anchors, class_predictions, y)
cls_target: nd.array
Output targets from network.training_targets(default_anchors, class_predictions, y)
x: nd.array
The input images
y: nd.array
The actual labels
Returns
-------
output_image: np.array
The images with the predicted and actual bounding boxes drawn on
number_of_bbs: int
The number of predicting bounding boxes
'''
output = MultiBoxDetection(*[cls_probs, box_predictions, default_anchors],
force_suppress=True,
clip=False)
output = box_nms(output,
overlap_thresh=overlap_thres,
valid_thresh=min_c,
topk=topk)
output = output.asnumpy()
number_of_bbs = 0
predicted_bb = []
for b in range(output.shape[0]):
predicted_bb_ = output[b, output[b, :, 0] != -1]
predicted_bb_ = predicted_bb_[:, 2:]
number_of_bbs += predicted_bb_.shape[0]
predicted_bb_[:, 2] = predicted_bb_[:, 2] - predicted_bb_[:, 0]
predicted_bb_[:, 3] = predicted_bb_[:, 3] - predicted_bb_[:, 1]
predicted_bb.append(predicted_bb_)
labels = y[:, :, 1:].asnumpy()
labels[:, :, 2] = labels[:, :, 2] - labels[:, :, 0]
labels[:, :, 3] = labels[:, :, 3] - labels[:, :, 1]
output_image = draw_boxes_on_image(predicted_bb, labels, x.asnumpy())
output_image[output_image < 0] = 0
output_image[output_image > 1] = 1
return output_image, number_of_bbs
# print("train_cls_loss: %5f, train_box_loss %5f,"
# "time %.1f sec"
# % (nd.mean(loss_cls).asscalar(), nd.mean(loss_bbox).asscalar(), time.time() - tic))
if epoch % checkpoint_period == 0:
net.save_params(filename='output/exp1/ssd_{}_{}_epoch{}.params'.format(net.network,
net.data_shape, epoch))
if val_iter is not None:
for i, batch in enumerate(val_iter):
x = batch.data[0].as_in_context(ctx) # 32 x 3 x 300 x 300
y = batch.label[0].as_in_context(ctx) # 32 x 43 x 8
anchors, class_preds, box_preds = net(x)
cls_probs = mx.nd.SoftmaxActivation(data=class_preds.transpose((0,2,1)), mode='channel')
det = MultiBoxDetection(*[cls_probs, box_preds, anchors], \
name="detection", nms_threshold=0.45, force_suppress=False,
variances=(0.1, 0.1, 0.2, 0.2), nms_topk=400)
val_metric.update(labels=[y], preds=[det])
names, values = val_metric.get()
# epoch_str = ("Epoch %d. Loss: %f, Train acc %f, Valid acc %f, "
# % (epoch, train_loss / len(train_data),
# train_acc / len(train_data), valid_acc))
for name, value in zip(names, values):
logger.info('Epoch[{}] Validation-{}={}'.format(epoch, name, value))
else:
logger.info ("Epoch %2d. train_cls_loss: %5f, train_box_loss %5f,"
" time %.1f sec"
% (epoch, train_cls_loss / i, train_bbox_loss / i,
time.time()-tic))
logger.info ('Epoch %2d, train %s %.2f, %s %.5f, time %.1f sec' % (
def predict(net, data):
anchors, box_preds, cls_preds = net(data)
cls_probs = nd.SoftmaxActivation(cls_preds.transpose((0, 2, 1)), mode='channel')
out = MultiBoxDetection(cls_probs, box_preds, anchors, force_suppress=True, clip=False, nms_threshold=0.45)
return out