def multi_detect_input(im):
#print(im.shape)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data_idx = [{"data": im_tensor, "im_info": im_info}]
data_idx = [[
mx.nd.array(data_idx[i][name]) for name in data_names
] for i in xrange(len(data_idx))]
data_batch = mx.io.DataBatch(
data=[data_idx[0]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data_idx[0])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
return data_batch, scales
def predict(self, imarray, pixel_means, pixel_stds, crop_size=512, color_scale=-1,
feature_ratio= 2.0 / 3, num_steps=1, output_name="softmax",feature_stride = 8):
im_tensor = transform(imarray, pixel_means, color_scale=color_scale, pixel_stds=pixel_stds)
long_size = max(im_tensor[2:])
if(long_size<crop_size):
return self.predict_patch(im_tensor,feature_stride,num_steps,output_name)
def get_data_tensor_info(self, im):
im_, im_scale = resize(im, self.target_size, self.max_size, stride=self.image_stride)
im_tensor = transform(im_, self.pixel_means)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data_tensor_info = [mx.nd.array(im_tensor), mx.nd.array(im_info)]
return data_tensor_info
def generate_batch(im):
"""
preprocess image, return batch
:param im: cv2.imread returns [height, width, channel] in BGR
:return:
data_batch: MXNet input batch
data_names: names in data_batch
im_scale: float number
"""
SHORT_SIDE = config.SCALES[0][0]
LONG_SIDE = config.SCALES[0][1]
PIXEL_MEANS = config.network.PIXEL_MEANS
DATA_NAMES = ['data', 'im_info']
im_array, im_scale = resize(im, SHORT_SIDE, LONG_SIDE)
im_array = transform(im_array, PIXEL_MEANS)
im_info = np.array([[im_array.shape[2], im_array.shape[3], im_scale]],
dtype=np.float32)
data = [[mx.nd.array(im_array), mx.nd.array(im_info)]]
data_shapes = [[('data', im_array.shape), ('im_info', im_info.shape)]]
data_batch = mx.io.DataBatch(data=data,
label=[None],
provide_data=data_shapes,
provide_label=[None])
return data_batch, DATA_NAMES, [im_scale]
def det(mod, fn):
raw_img = cv2.imread(fn)
if raw_img.shape[0] < raw_img.shape[1]:
raw_img = cv2.copyMakeBorder(raw_img,0
,raw_img.shape[1]-raw_img.shape[0], 0, 0, cv2.BORDER_CONSTANT)
im_shape = [IMG_H,IMG_W] # reverse order
img = cv2.resize(raw_img, (IMG_H,IMG_W))
raw_h = img.shape[0]
raw_w = img.shape[1]
im_tensor = image.transform(img, [124,117,104], 0.0167)
im_info = np.array([[ IMG_H, IMG_W, 4.18300658e-01]])
batch = mx.io.DataBatch([mx.nd.array(im_tensor), mx.nd.array(im_info)])
start = time.time()
mod.forward(batch)
output_names = mod.output_names
output_tensor = mod.get_outputs()
mod.get_outputs()[0].wait_to_read()
print ("time", time.time()-start, "secs.")
output = dict(zip(output_names ,output_tensor))
rois = output['rois_output'].asnumpy()[:, 1:]
scores = output['cls_prob_reshape_output'].asnumpy()[0]
bbox_deltas = output['bbox_pred_reshape_output'].asnumpy()[0]
pred_boxes = bbox_pred(rois, bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, im_shape[-2:])
num_classes = 2
all_cls_dets = [[] for _ in range(num_classes)]
for j in range(1, num_classes):
indexes = np.where(scores[:, j] > 0.1)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = pred_boxes[indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores)).copy()
all_cls_dets[j] = cls_dets
for idx_class in range(1, num_classes):
nms = py_nms_wrapper(0.3)
keep = nms(all_cls_dets[idx_class])
all_cls_dets[idx_class] = all_cls_dets[idx_class][keep, :]
for i in range(all_cls_dets[1].shape[0]):
cv2.rectangle(img, (int(all_cls_dets[1][i][0]), int(all_cls_dets[1][i][1]))
,(int(all_cls_dets[1][i][2]), int(all_cls_dets[1][i][3])),(0,0,255),1)
cv2.imshow("w", img)
cv2.waitKey()
def main(im_name, frame, score):
data = []
# only resize input image to target size and return scale
im, im_scale = resize(frame, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
data_batch = mx.io.DataBatch(data=[data[0]], label=[], pad=0, index=0,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[0])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
tic()
scores, boxes, masks, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
im_shapes = [data_batch.data[i][0].shape[2:4] for i in xrange(len(data_batch.data))]
masks = masks[0][:, 1:, :, :]
im_height = np.round(im_shapes[0][0] / scales[0]).astype('int')
im_width = np.round(im_shapes[0][1] / scales[0]).astype('int')
# print (im_height, im_width)
boxes = clip_boxes(boxes[0], (im_height, im_width))
result_masks, result_dets = cpu_mask_voting(masks, boxes, scores[0], num_classes,
100, im_width, im_height,
config.TEST.NMS, config.TEST.MASK_MERGE_THRESH,
config.BINARY_THRESH)
dets = [result_dets[j] for j in range(1, num_classes)]
masks = [result_masks[j][:, 0, :, :] for j in range(1, num_classes)]
print 'testing {} {:.4f}s'.format(im_name, toc())
min_confidence = score
# visualize
for i in xrange(len(dets)):
keep = np.where(dets[i][:,-1] > min_confidence)
dets[i] = dets[i][keep]
masks[i] = masks[i][keep]
'''
dets: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
[x1 y1] - upper left corner of object
[x2 y2] - lower right corner of object
masks: [ numpy.ndarray([21, 21]) for j in classes ]
confidence that pixel belongs to object
'''
for i in range(len(dets)):
if len(dets[i]) > 0:
for j in range(len(dets[i])):
print('{name}: {score} ({loc})'.format(name = classes[i], score = dets[i][j][-1], loc = dets[i][j][:-1].tolist()))
def fcis_seg(image, classes, predictor, args):
num_classes = len(classes) + 1
data = []
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(image, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
start = time.time()
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
data_names = ['data', 'im_info']
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
data_batch = mx.io.DataBatch(data=[data[0]], label=[], pad=0, index=0,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[0])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
scores, boxes, masks, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
im_shapes = [data_batch.data[i][0].shape[2:4] for i in xrange(len(data_batch.data))]
if not config.TEST.USE_MASK_MERGE:
all_boxes = [[] for _ in xrange(num_classes)]
all_masks = [[] for _ in xrange(num_classes)]
nms = py_nms_wrapper(config.TEST.NMS)
for j in range(1, num_classes):
indexes = np.where(scores[0][:, j] > 0.7)[0]
cls_scores = scores[0][indexes, j, np.newaxis]
cls_masks = masks[0][indexes, 1, :, :]
try:
if config.CLASS_AGNOSTIC:
cls_boxes = boxes[0][indexes, :]
else:
raise Exception()
except:
cls_boxes = boxes[0][indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
all_boxes[j] = cls_dets[keep, :]
all_masks[j] = cls_masks[keep, :]
dets = [all_boxes[j] for j in range(1, num_classes)]
masks = [all_masks[j] for j in range(1, num_classes)]
else:
masks = masks[0][:, 1:, :, :]
im_height = np.round(im_shapes[0][0] / scales[0]).astype('int')
im_width = np.round(im_shapes[0][1] / scales[0]).astype('int')
# print (im_height, im_width)
boxes = clip_boxes(boxes[0], (im_height, im_width))
result_masks, result_dets = gpu_mask_voting(masks, boxes, scores[0], num_classes,
100, im_width, im_height,
config.TEST.NMS, config.TEST.MASK_MERGE_THRESH,
config.BINARY_THRESH, 0)
dets = [result_dets[j] for j in range(1, num_classes)]
masks = [result_masks[j][:, 0, :, :] for j in range(1, num_classes)]
cods, bimsks, names = decode_mask(im, dets, masks, classes, config, args)
return cods, bimsks, names
def main():
# get symbol
pprint.pprint(config)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol_rfcn(config, is_train=False)
# load demo data
image_names = ['000057.jpg', '000149.jpg', '000351.jpg', '002535.jpg']
image_all = []
# ground truth boxes
gt_boxes_all = [np.array([[132, 52, 384, 357]]), np.array([[113, 1, 350, 360]]),
np.array([[0, 27, 329, 155]]), np.array([[8, 40, 499, 289]])]
gt_classes_all = [np.array([3]), np.array([16]), np.array([7]), np.array([12])]
data = []
for idx, im_name in enumerate(image_names):
assert os.path.exists(cur_path + '/../demo/deform_psroi/' + im_name), \
('%s does not exist'.format('../demo/deform_psroi/' + im_name))
im = cv2.imread(cur_path + '/../demo/deform_psroi/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
image_all.append(im)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
gt_boxes = gt_boxes_all[idx]
gt_boxes = np.round(gt_boxes * im_scale)
data.append({'data': im_tensor, 'rois': np.hstack((np.zeros((gt_boxes.shape[0], 1)), gt_boxes))})
# get predictor
data_names = ['data', 'rois']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/deform_psroi', 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
# test
for idx, _ in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
output = predictor.predict(data_batch)
cls_offset = output[0]['rfcn_cls_offset_output'].asnumpy()
im = image_all[idx]
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
boxes = gt_boxes_all[idx]
show_dpsroi_offset(im, boxes, cls_offset, gt_classes_all[idx])
def main():
# get symbol
pprint.pprint(config)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# load demo data
image_names = ['000240.jpg', '000437.jpg', '004072.jpg', '007912.jpg']
image_all = []
data = []
for im_name in image_names:
assert os.path.exists(cur_path + '/../demo/deform_conv/' + im_name), \
('%s does not exist'.format('../demo/deform_conv/' + im_name))
im = cv2.imread(cur_path + '/../demo/deform_conv/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
image_all.append(im)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/deform_conv', 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
# test
for idx, _ in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
output = predictor.predict(data_batch)
res5a_offset = output[0]['res5a_branch2b_offset_output'].asnumpy()
res5b_offset = output[0]['res5b_branch2b_offset_output'].asnumpy()
res5c_offset = output[0]['res5c_branch2b_offset_output'].asnumpy()
im = image_all[idx]
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
show_dconv_offset(im, [res5c_offset, res5b_offset, res5a_offset])
def main():
# get symbol
pprint.pprint(config)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# load demo data
image_names = ['000240.jpg', '000437.jpg', '004072.jpg', '007912.jpg']
image_all = []
data = []
for im_name in image_names:
assert os.path.exists(cur_path + '/../demo/deform_conv/' + im_name), \
('%s does not exist'.format('../demo/deform_conv/' + im_name))
im = cv2.imread(cur_path + '/../demo/deform_conv/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
image_all.append(im)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/deform_conv', 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
# test
for idx, _ in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
output = predictor.predict(data_batch)
res5a_offset = output[0]['res5a_branch2b_offset_output'].asnumpy()
res5b_offset = output[0]['res5b_branch2b_offset_output'].asnumpy()
res5c_offset = output[0]['res5c_branch2b_offset_output'].asnumpy()
im = image_all[idx]
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
show_dconv_offset(im, [res5c_offset, res5b_offset, res5a_offset])
def _load_frame(self, idx):
im = self.images[idx]
target_size = cfg.SCALES[0][0]
max_size = cfg.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, cfg.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
feat_stride = float(cfg.network.RCNN_FEAT_STRIDE)
return mx.nd.array(im_tensor), mx.nd.array(im_info)
def predict(self, im):
im, im_scale = resize(im,
self.scales[0],
self.scales[1],
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data = {'data': im_tensor, 'im_info': im_info}
data = [[
mx.nd.array(data[self.data_names[0]]),
mx.nd.array(data[self.data_names[1]])
]]
data_batch = mx.io.DataBatch(
data=data,
label=[],
pad=0,
index=0,
provide_data=[[(k, v.shape)
for k, v in zip(self.data_names, data[0])]],
provide_label=[None])
scores, boxes, data_dict = im_detect(self.predictor, data_batch,
self.data_names, [im_scale],
config)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:,
4:8] if config.CLASS_AGNOSTIC else boxes[:, j *
4:(j +
1) *
4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = self.nms(cls_dets)
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
res = {}
for idx, cls in enumerate(self.classes):
res['{}'.format(cls)] = dets_nms[idx].tolist()
logging.debug("Predictions: {}".format(res))
return res
def parfetch(self, iroidb):
# get testing data for multigpu
data, label = get_rpn_pair_mv_batch(iroidb, self.cfg)
#for k, v in data.items():
# print(k, v)
data_shape = {k: v.shape for k, v in data.items()}
#print(data_shape)
del data_shape['im_info']
del data_shape['data']
data_shape1 = copy.deepcopy(data_shape)
del data_shape1['eq_flag']
del data_shape1['motion_vector']
_, feat_shape, _ = self.feat_conv_3x3_relu.infer_shape(**data_shape1)
#print('feat_shape: ', feat_shape)
#print('shape: ', data['motion_vector'].shape)
#print("size: ", int(feat_shape[0][2]), int(feat_shape[0][3]))
data['motion_vector'] = data['motion_vector'].astype('float64')
data['motion_vector'] = cv2.resize(
data['motion_vector'],
(int(feat_shape[0][3]), int(feat_shape[0][2])),
interpolation=cv2.INTER_AREA)
#print('data[\'motion_vector\'].shape: ', data['motion_vector'].shape)
data['motion_vector'] = transform(data['motion_vector'], [0, 0])
#print('data[\'motion_vector\'].shape: ', data['motion_vector'].shape)
#print("data['motion_vector']: ", data['motion_vector'])
#data['motion_vector'] = cv2.resize(data['motion_vector'], (36, 63))
data_shape = {k: v.shape for k, v in data.items()}
#print(data_shape)
del data_shape['im_info']
del data_shape['data']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'],
self.cfg, self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean,
self.bbox_std)
return {'data': data, 'label': label}
def loadImage(self, im, bg):
self.im = im
self.fg = im
self.bg = bg
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
self.im, im_scale = resize(self.im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(self.im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
self.data = {'data': im_tensor, 'im_info': im_info}
self.data = [mx.nd.array(self.data[name]) for name in self.data_names]
def load_test(filename, short, max_size, mean, std):
# read and transform image
im_orig = imdecode(filename)
im, im_scale = resize(im_orig, short, max_size)
height, width = im.shape[:2]
im_info = mx.nd.array([height, width, im_scale])
# transform into tensor and normalize
im_tensor = transform(im, mean, std)
# for 1-batch inference purpose, cannot use batchify (or nd.stack) to expand dims
im_tensor = mx.nd.array(im_tensor).expand_dims(0)
im_info = mx.nd.array(im_info).expand_dims(0)
# transform cv2 BRG image to RGB for matplotlib
im_orig = im_orig[:, :, (2, 1, 0)]
return im_tensor, im_info, im_orig
def get_predictor(sym, image, arg_params, aux_params):
data = []
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(image, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
return predictor
def _load_frame(self, idx):
im_name = self.images_names[idx]
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if idx == 100:
im[...] = 0
target_size = cfg.SCALES[0][0]
max_size = cfg.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, cfg.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
feat_stride = float(cfg.network.RCNN_FEAT_STRIDE)
return mx.nd.array(im_tensor), mx.nd.array(im_info)
def load_data_and_get_predictor(self, image_names):
# load demo data
#image_names = ['COCO_test2015_000000000891.jpg',
# 'COCO_test2015_000000001669.jpg']
data = []
for im_name in image_names:
#assert os.path.exists(
# cur_path + '/../demo/' + im_name), ('%s does not exist'.format('../demo/' + im_name))
#im = cv2.imread(cur_path + '/../demo/' + im_name,
# cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
im = cv2.imread(im_name, cv2.IMREAD_COLOR |
cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
self.data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in self.data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max(
[v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(self.data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
self.predictor = Predictor(self.sym, self.data_names, label_names,
context=[mx.gpu(1)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=self.arg_params, aux_params=self.aux_params)
self.nms = gpu_nms_wrapper(config.TEST.NMS, 0)
return data
def generate_batch_V2(im,num_gpu):
"""
preprocess image, return batch
:param im: cv2.imread returns [height, width, channel] in BGR
:return:
data_batch: MXNet input batch
data_names: names in data_batch
im_scale: float number
"""
array_list , info_list =[],[]
for idx in range(0,num_gpu):
SHORT_SIDE = config.SCALES[0][0]
LONG_SIDE = config.SCALES[0][1]
PIXEL_MEANS = config.network.PIXEL_MEANS
DATA_NAMES = ['data', 'im_info']
im_array, im_scale = resize(im[idx], SHORT_SIDE, LONG_SIDE)
im_array = transform(im_array, PIXEL_MEANS)
im_info = np.array([[im_array.shape[2], im_array.shape[3], im_scale]], dtype=np.float32)
array_list.append(im_array)
info_list.append(im_info)
data = [[mx.nd.array(_), mx.nd.array(__)] for _ in array_list,for __ in info_list]
def read_data(self, im_name): #data (1,3, 562,1000)
data_names = ['data', 'im_info', 'data_cache', 'feat_cache']
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
print("About to read ", im_name)
im = cv2.imread(im_name,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = self.cfg.SCALES[0][0]
max_size = self.cfg.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=self.cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, self.cfg.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
d = {
'data': mx.nd.array(im_tensor),
'im_info': mx.nd.array(im_info),
'data_cache': mx.nd.array(im_tensor),
'feat_cache': mx.nd.array(im_tensor)
}
return [d[data_name] for data_name in data_names]
def main():
# get symbol
pprint.pprint(cfg)
cfg.symbol = 'resnet_v1_101_flownet_rfcn_online_train'
model = '/../model/rfcn_fgfa_flownet_vid_original'
all_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1
max_per_image = cfg.TEST.max_per_image
feat_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
aggr_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
feat_sym = feat_sym_instance.get_feat_symbol(cfg)
aggr_sym = aggr_sym_instance.get_aggregation_symbol(cfg)
# set up class names
num_classes = 31
classes = ['__background__','airplane', 'antelope', 'bear', 'bicycle',
'bird', 'bus', 'car', 'cattle',
'dog', 'domestic_cat', 'elephant', 'fox',
'giant_panda', 'hamster', 'horse', 'lion',
'lizard', 'monkey', 'motorcycle', 'rabbit',
'red_panda', 'sheep', 'snake', 'squirrel',
'tiger', 'train', 'turtle', 'watercraft',
'whale', 'zebra']
# load demo data
snippet_name = 'ILSVRC2015_val_00177001' #'ILSVRC2015_val_00007016'#'ILSVRC2015_val_00000004' # 'ILSVRC2015_val_00007016'# 'ILSVRC2015_val_00044006' #'ILSVRC2015_val_00007010' #'ILSVRC2015_val_00016002'
# ILSVRC2015_val_00010001: zebra,
# 37002:cat and fox, motion blur
# ILSVRC2015_val_00095000: fox, defocus
image_names = glob.glob(cur_path + '/../demo/' + snippet_name + '/*.JPEG')
image_names.sort()
output_dir = cur_path + '/../demo/test_'# rfcn_fgfa_online_train_'
output_dir_ginst = cur_path + '/../demo/test_ginst_' # rfcn_fgfa_online_train_'
output_dir_linst = cur_path + '/../demo/test_linst_'
if (cfg.TEST.SEQ_NMS):
output_dir += 'SEQ_NMS_'
output_dir_ginst += 'SEQ_NMS_'
output_dir += snippet_name + '/'
output_dir_ginst += snippet_name + '/'
output_dir_linst += snippet_name + '/'
data = []
for im_name in image_names:
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = cfg.SCALES[0][0]
max_size = cfg.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, cfg.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
feat_stride = float(cfg.network.RCNN_FEAT_STRIDE)
data.append({'data': im_tensor, 'im_info': im_info, 'data_cache': im_tensor, 'feat_cache': im_tensor})
# get predictor
print('get-predictor')
data_names = ['data', 'im_info', 'data_cache', 'feat_cache']
label_names = []
t1 = time.time()
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES]))),
('data_cache', (19, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES]))),
('feat_cache', ((19, cfg.network.FGFA_FEAT_DIM,
np.ceil(max([v[0] for v in cfg.SCALES]) / feat_stride).astype(np.int),
np.ceil(max([v[1] for v in cfg.SCALES]) / feat_stride).astype(np.int))))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for _ in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + model, 0, process=True)
#add parameters for instance cls & regression
arg_params['rfcn_ibbox_bias'] = arg_params['rfcn_bbox_bias'].copy() #deep copy
arg_params['rfcn_ibbox_weight'] = arg_params['rfcn_bbox_weight'].copy() #deep copy
max_inst = cfg.TEST.NUM_INSTANCES
feat_predictors = Predictor(feat_sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
aggr_predictors = Predictor(aggr_sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
nms = py_nms_wrapper(cfg.TEST.NMS)
# First frame of the video
idx = 0
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
all_boxes = [[[] for _ in range(len(data))]
for _ in range(num_classes)]
all_boxes_inst = [[[] for _ in range(len(data))]
for _ in range(num_classes)]
ginst_mem = [] # list for instance class
sim_array_global = [] # similarity array list
ginst_ID = 0
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
# append cfg.TEST.KEY_FRAME_INTERVAL padding images in the front (first frame)
while len(data_list) < cfg.TEST.KEY_FRAME_INTERVAL:
data_list.append(image)
feat_list.append(feat)
vis = True
file_idx = 0
thresh = (1e-3)
for idx, element in enumerate(data):
data_batch = mx.io.DataBatch(data=[element], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, element)]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
if(idx != len(data)-1):
if len(data_list) < all_frame_interval - 1:
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
else:
#if file_idx ==15:
# print( '%d frame' % (file_idx))
#################################################
# main part of the loop
#################################################
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch) #put 19 data & feat list into data_batch
#aggr_predictors._mod.forward(data_batch) #sidong
#zxcv= aggr_predictors._mod.get_outputs(merge_multi_context=False)#sidong
pred_result = im_detect_all(aggr_predictors, data_batch, data_names, scales, cfg) # get box result [[scores, pred_boxes, rois, data_dict, iscores, ipred_boxes, cropped_embed]]
data_batch.data[0][-2] = None # 19 frames of data possesses much memory, so clear it
data_batch.provide_data[0][-2] = ('data_cache', None) # also clear shape info of data
data_batch.data[0][-1] = None
data_batch.provide_data[0][-1] = ('feat_cache', None)
ginst_ID_prev = ginst_ID
ginst_ID, out_im, out_im2, out_im_linst = process_link_pred_result(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes,
all_boxes_inst, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales, ginst_mem, sim_array_global, ginst_ID)
#out_im2 = process_pred_result_rois(pred_result, cfg.TEST.RPN_NMS_THRESH, cfg, nms, all_rois, file_idx, max_per_image,
# data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
ginst_ID_now = ginst_ID
init_inst_params(ginst_mem, ginst_ID_prev, ginst_ID_now, max_inst, aggr_predictors, arg_params)
total_time = time.time()-t1
if (cfg.TEST.SEQ_NMS==False):
if cfg.TEST.DISPLAY[0]:
save_image(output_dir, file_idx, out_im)
if cfg.TEST.DISPLAY[1]:
save_image(output_dir_ginst, file_idx, out_im2)
if cfg.TEST.DISPLAY[2]:
save_image(output_dir_linst, file_idx, out_im_linst)
#testing by metric
print( 'testing {} {:.4f}s'.format(str(file_idx)+'.JPEG', total_time /(file_idx+1)))
file_idx += 1
else:
#################################################
# end part of a video #
#################################################
end_counter = 0
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
while end_counter < cfg.TEST.KEY_FRAME_INTERVAL + 1:
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
pred_result = im_detect_all(aggr_predictors, data_batch, data_names, scales, cfg)
ginst_ID_prev = ginst_ID
ginst_ID, out_im, out_im2, out_im_linst = process_link_pred_result(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes,
all_boxes_inst, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales,
ginst_mem, sim_array_global, ginst_ID)
# out_im2 = process_pred_result_rois(pred_result, cfg.TEST.RPN_NMS_THRESH, cfg, nms, all_rois, file_idx, max_per_image,
# data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
ginst_ID_now = ginst_ID
init_inst_params(ginst_mem, ginst_ID_prev, ginst_ID_now, max_inst, aggr_predictors ,arg_params)
total_time = time.time() - t1
if (cfg.TEST.SEQ_NMS == False):
if cfg.TEST.DISPLAY[0]:
save_image(output_dir, file_idx, out_im)
if cfg.TEST.DISPLAY[1]:
save_image(output_dir_ginst, file_idx, out_im2)
if cfg.TEST.DISPLAY[2]:
save_image(output_dir_linst, file_idx, out_im_linst)
print( 'testing {} {:.4f}s'.format(str(file_idx)+'.JPEG', total_time / (file_idx+1)))
file_idx += 1
end_counter += 1
if(cfg.TEST.SEQ_NMS):
video = [all_boxes[j][:] for j in range(1, num_classes)]
dets_all = seq_nms(video)
for cls_ind, dets_cls in enumerate(dets_all):
for frame_ind, dets in enumerate(dets_cls):
keep = nms(dets)
all_boxes[cls_ind + 1][frame_ind] = dets[keep, :]
for idx in range(len(data)):
boxes_this_image = [[]] + [all_boxes[j][idx] for j in range(1, num_classes)]
out_im = draw_all_detection(data[idx][0].asnumpy(), boxes_this_image, classes, scales[0], cfg)
save_image(output_dir, idx, out_im)
print('done')
def main():
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_flownet_deeplab'
model1 = '/../model/rfcn_dff_flownet_vid'
model2 = '/../model/deeplab_dcn_cityscapes'
sym_instance = eval(config.symbol + '.' + config.symbol)()
key_sym = sym_instance.get_key_test_symbol(config)
cur_sym = sym_instance.get_cur_test_symbol(config)
# settings
num_classes = 19
interv = args.interval
num_ex = args.num_ex
# load demo data
image_names = sorted(
glob.glob(cur_path +
'/../demo/cityscapes_data/cityscapes_frankfurt_all_i' +
str(interv) + '/*.png'))
image_names = image_names[:interv * num_ex]
label_files = sorted(
glob.glob(
cur_path +
'/../demo/cityscapes_data/cityscapes_frankfurt_labels_all/*.png'))
output_dir = cur_path + '/../demo/deeplab_dff/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
key_frame_interval = interv
#
data = []
key_im_tensor = None
for idx, im_name in enumerate(image_names):
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
if idx % key_frame_interval == 0:
key_im_tensor = im_tensor
data.append({
'data':
im_tensor,
'im_info':
im_info,
'data_key':
key_im_tensor,
'feat_key':
np.zeros((1, config.network.DFF_FEAT_DIM, 1, 1))
})
# get predictor
data_names = ['data', 'data_key', 'feat_key']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[
('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
('data_key', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
# models: rfcn_dff_flownet_vid, deeplab_cityscapes
arg_params, aux_params = load_param_multi(cur_path + model1,
cur_path + model2,
0,
process=True)
key_predictor = Predictor(key_sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
cur_predictor = Predictor(cur_sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# warm up
for j in xrange(2):
data_batch = mx.io.DataBatch(data=[data[j]],
label=[],
pad=0,
index=0,
provide_data=[[
(k, v.shape)
for k, v in zip(data_names, data[j])
]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
if j % key_frame_interval == 0:
# scores, boxes, data_dict, feat = im_detect(key_predictor, data_batch, data_names, scales, config)
output_all, feat = im_segment(key_predictor, data_batch)
output_all = [
mx.ndarray.argmax(output['croped_score_output'],
axis=1).asnumpy() for output in output_all
]
else:
data_batch.data[0][-1] = feat
data_batch.provide_data[0][-1] = ('feat_key', feat.shape)
# scores, boxes, data_dict, _ = im_detect(cur_predictor, data_batch, data_names, scales, config)
output_all, _ = im_segment(cur_predictor, data_batch)
output_all = [
mx.ndarray.argmax(output['croped_score_output'],
axis=1).asnumpy() for output in output_all
]
print "warmup done"
# test
time = 0
count = 0
hist = np.zeros((num_classes, num_classes))
lb_idx = 0
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(
data=[data[idx]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
tic()
if idx % key_frame_interval == 0:
print '\nframe {} (key)'.format(idx)
# scores, boxes, data_dict, feat = im_detect(key_predictor, data_batch, data_names, scales, config)
output_all, feat = im_segment(key_predictor, data_batch)
output_all = [
mx.ndarray.argmax(output['croped_score_output'],
axis=1).asnumpy() for output in output_all
]
else:
print '\nframe {} (intermediate)'.format(idx)
data_batch.data[0][-1] = feat
data_batch.provide_data[0][-1] = ('feat_key', feat.shape)
# scores, boxes, data_dict, _ = im_detect(cur_predictor, data_batch, data_names, scales, config)
output_all, _ = im_segment(cur_predictor, data_batch)
output_all = [
mx.ndarray.argmax(output['croped_score_output'],
axis=1).asnumpy() for output in output_all
]
elapsed = toc()
time += elapsed
count += 1
print 'testing {} {:.4f}s [{:.4f}s]'.format(im_name, elapsed,
time / count)
pred = np.uint8(np.squeeze(output_all))
segmentation_result = Image.fromarray(pred)
pallete = getpallete(256)
segmentation_result.putpalette(pallete)
_, im_filename = os.path.split(im_name)
segmentation_result.save(output_dir + '/seg_' + im_filename)
label = None
_, lb_filename = os.path.split(label_files[lb_idx])
im_comps = im_filename.split('_')
lb_comps = lb_filename.split('_')
# if annotation available for frame
if im_comps[1] == lb_comps[1] and im_comps[2] == lb_comps[2]:
print 'label {}'.format(lb_filename)
label = np.asarray(Image.open(label_files[lb_idx]))
if lb_idx < len(label_files) - 1:
lb_idx += 1
if label is not None:
curr_hist = fast_hist(pred.flatten(), label.flatten(), num_classes)
hist += curr_hist
print 'mIoU {mIoU:.3f}'.format(
mIoU=round(np.nanmean(per_class_iu(curr_hist)) * 100, 2))
print '(cum) mIoU {mIoU:.3f}'.format(
mIoU=round(np.nanmean(per_class_iu(hist)) * 100, 2))
ious = per_class_iu(hist) * 100
print ' '.join('{:.03f}'.format(i) for i in ious)
print '===> final mIoU {mIoU:.3f}'.format(mIoU=round(np.nanmean(ious), 2))
print 'done'
mod_key.set_params(arg_params, aux_params)
#%%
data = []
key_im_tensor = None
for idx, im_name in enumerate(image_names):
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
# print(im_info.shape)
# print im_info
if idx % key_frame_interval == 0:
key_im_tensor = im_tensor
data.append({
'data': im_tensor,
'im_info': im_info,
'data_key': key_im_tensor,
'feat_key': np.zeros((1, config.network.DFF_FEAT_DIM, 36, 63))
})
# get predictor
# data_names = ['data', 'im_info', 'data_key', 'feat_key']
def run_detection(im_root, result_root, conf_threshold):
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_rfcn_dcn' if not args.rfcn_only else 'resnet_v1_101_rfcn'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# set up class names
num_classes = 81
classes = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
print('detection in {}'.format(im_root))
im_names = sorted(os.listdir(im_root))
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = []
for idx, im_name in enumerate(im_names[:2]):
im_file = os.path.join(im_root, im_name)
im = cv2.imread(im_file,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(
cur_path + '/../model/' +
('rfcn_dcn_coco' if not args.rfcn_only else 'rfcn_coco'),
config.TEST.test_epoch,
process=True)
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
# nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# nms = soft_nms_wrapper(config.TEST.NMS, method=2)
nms = gpu_soft_nms_wrapper(config.TEST.NMS, method=2, device_id=0)
nms_t = Timer()
for idx, im_name in enumerate(im_names):
im_file = os.path.join(im_root, im_name)
im = cv2.imread(im_file,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
origin_im = im.copy()
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
# input
data = [mx.nd.array(im_tensor), mx.nd.array(im_info)]
data_batch = mx.io.DataBatch(data=[data],
label=[],
pad=0,
index=idx,
provide_data=[[
(k, v.shape)
for k, v in zip(data_names, data)
]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
tic()
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names,
scales, config)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:,
4:8] if config.CLASS_AGNOSTIC else boxes[:, j *
4:(j +
1) *
4]
cls_dets = np.hstack((cls_boxes, cls_scores))
nms_t.tic()
keep = nms(cls_dets)
nms_t.toc()
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
print 'testing {} {:.2f}ms'.format(im_name, toc() * 1000)
print 'nms: {:.2f}ms'.format(nms_t.total_time * 1000)
nms_t.clear()
# save results
person_dets = dets_nms[0]
with open(os.path.join(result_root, '{:04d}.txt'.format(idx)),
'w') as f:
f.write('{}\n'.format(len(person_dets)))
for det in person_dets:
x1, y1, x2, y2, s = det
w = x2 - x1
h = y2 - y1
f.write('0 {} {} {} {} {}\n'.format(s, w, h, x1, y1))
# visualize
im = origin_im
# im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = show_boxes_cv2(im, dets_nms, classes, 1)
cv2.imshow('det', im)
cv2.waitKey(1)
def main():
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_rfcn_dcn' if not args.rfcn_only else 'resnet_v1_101_rfcn'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# set up class names
num_classes = 81
classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut',
'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse',
'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
# load demo data
image_names = ['COCO_test2015_000000000891.jpg', 'COCO_test2015_000000001669.jpg']
data = []
for im_name in image_names:
assert os.path.exists(cur_path + '/../demo/' + im_name), ('%s does not exist'.format('../demo/' + im_name))
im = cv2.imread(cur_path + '/../demo/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/' + ('rfcn_dcn_coco' if not args.rfcn_only else 'rfcn_coco'), 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# warm up
for j in xrange(2):
data_batch = mx.io.DataBatch(data=[data[0]], label=[], pad=0, index=0,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[0])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
# test
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
tic()
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:, 4:8] if config.CLASS_AGNOSTIC else boxes[:, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
print 'testing {} {:.4f}s'.format(im_name, toc())
# visualize
im = cv2.imread(cur_path + '/../demo/' + im_name)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
show_boxes(im, dets_nms, classes, 1)
print 'done'
def main():
global classes
assert os.path.exists(args.input), ('%s does not exist'.format(args.input))
im = cv2.imread(args.input,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
arr = np.array(im)
origin_width, origin_height, _ = arr.shape
portion = smart_chipping(origin_width, origin_height)
# manually update the configuration
# print(config.SCALES[0][0])
# TODO: note this is hard coded and assume there are three values for the SCALE configuration
config.SCALES[0] = (portion, portion, portion)
# config.max_per_image =
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_fpn_dcn_rcnn'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# load demo data
data = []
# portion = args.chip_size
cwn, chn = (portion, portion)
wn, hn = (int(origin_width / cwn), int(origin_height / chn))
padding_y = int(
math.ceil(float(origin_height) / chn) * chn - origin_height)
padding_x = int(math.ceil(float(origin_width) / cwn) * cwn - origin_width)
print("padding_y,padding_x, origin_height, origin_width", padding_y,
padding_x, origin_height, origin_width)
# top, bottom, left, right - border width in number of pixels in corresponding directions
im = cv2.copyMakeBorder(im,
0,
padding_x,
0,
padding_y,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
# the section below could be optimized. but basically the idea is to re-calculate all the values
arr = np.array(im)
width, height, _ = arr.shape
cwn, chn = (portion, portion)
wn, hn = (int(width / cwn), int(height / chn))
image_list = chip_image(im, (portion, portion))
for im in image_list:
target_size = portion
max_size = portion
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
# print("im.shape,im_scale",im.shape,im_scale)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/' +
('fpn_dcn_xview_480_640_800_alltrain'),
11,
process=True)
# arg_params, aux_params = load_param(cur_path + '/../model/' + ('fpn_dcn_coco' if not args.fpn_only else 'fpn_coco'), 0, process=True)
print("loading parameter done")
if args.cpu_only:
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.cpu()],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = py_nms_wrapper(config.TEST.NMS)
else:
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(args.gpu_index)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
num_preds = int(5000 * math.ceil(float(portion) / 400))
# test
boxes, scores, classes = generate_detections(data, data_names, predictor,
config, nms, image_list,
num_preds)
#Process boxes to be full-sized
print("boxes shape is", boxes.shape, "wn, hn", wn, hn, "width, height",
width, height)
bfull = boxes.reshape((wn, hn, num_preds, 4))
for i in range(wn):
for j in range(hn):
bfull[i, j, :, 0] += j * cwn
bfull[i, j, :, 2] += j * cwn
bfull[i, j, :, 1] += i * chn
bfull[i, j, :, 3] += i * chn
# clip values
bfull[i, j, :, 0] = np.clip(bfull[i, j, :, 0], 0, origin_height)
bfull[i, j, :, 2] = np.clip(bfull[i, j, :, 2], 0, origin_height)
bfull[i, j, :, 1] = np.clip(bfull[i, j, :, 1], 0, origin_width)
bfull[i, j, :, 3] = np.clip(bfull[i, j, :, 3], 0, origin_width)
bfull = bfull.reshape((hn * wn, num_preds, 4))
scores = scores.reshape((hn * wn, num_preds))
classes = classes.reshape((hn * wn, num_preds))
#only display boxes with confidence > .5
# print(bfull, scores, classes)
#bs = bfull[scores > 0.08]
#cs = classes[scores>0.08]
#print("bfull.shape,scores.shape, bs.shape",bfull.shape,scores.shape, bs.shape)
# s = im_name
# draw_bboxes(arr,bs,cs).save("/tmp/"+s[0].split(".")[0] + ".png")
#scoring_line_threshold = 11000
#if bs.shape[0] > scoring_line_threshold:
# too many predictions, we should trim the low confidence ones
with open(args.output, 'w') as f:
for i in range(bfull.shape[0]):
for j in range(bfull[i].shape[0]):
#box should be xmin ymin xmax ymax
box = bfull[i, j]
class_prediction = classes[i, j]
score_prediction = scores[i, j]
if int(class_prediction) != 0:
f.write('%d %d %d %d %d %f \n' % \
(box[0], box[1], box[2], box[3], int(class_prediction), score_prediction))
print('done')
def main():
# get symbol
pprint.pprint(cfg)
cfg.symbol = 'resnet_v1_101_flownet_rfcn'
model = '/../model/rfcn_fgfa_flownet_vid'
all_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1
max_per_image = cfg.TEST.max_per_image
feat_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
aggr_sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
feat_sym = feat_sym_instance.get_feat_symbol(cfg)
aggr_sym = aggr_sym_instance.get_aggregation_symbol(cfg)
# set up class names
num_classes = 31
classes = ['__background__','airplane', 'antelope', 'bear', 'bicycle',
'bird', 'bus', 'car', 'cattle',
'dog', 'domestic_cat', 'elephant', 'fox',
'giant_panda', 'hamster', 'horse', 'lion',
'lizard', 'monkey', 'motorcycle', 'rabbit',
'red_panda', 'sheep', 'snake', 'squirrel',
'tiger', 'train', 'turtle', 'watercraft',
'whale', 'zebra']
# load demo data
image_names = glob.glob(cur_path + '/../demo/ILSVRC2015_val_00007010/*.JPEG')
output_dir = cur_path + '/../demo/rfcn_fgfa/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
data = []
for im_name in image_names:
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = cfg.SCALES[0][0]
max_size = cfg.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, cfg.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
feat_stride = float(cfg.network.RCNN_FEAT_STRIDE)
data.append({'data': im_tensor, 'im_info': im_info, 'data_cache': im_tensor, 'feat_cache': im_tensor})
# get predictor
print 'get-predictor'
data_names = ['data', 'im_info', 'data_cache', 'feat_cache']
label_names = []
t1 = time.time()
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES]))),
('data_cache', (19, 3, max([v[0] for v in cfg.SCALES]), max([v[1] for v in cfg.SCALES]))),
('feat_cache', ((19, cfg.network.FGFA_FEAT_DIM,
np.ceil(max([v[0] for v in cfg.SCALES]) / feat_stride).astype(np.int),
np.ceil(max([v[1] for v in cfg.SCALES]) / feat_stride).astype(np.int))))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for _ in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + model, 0, process=True)
feat_predictors = Predictor(feat_sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
aggr_predictors = Predictor(aggr_sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
nms = py_nms_wrapper(cfg.TEST.NMS)
# First frame of the video
idx = 0
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
all_boxes = [[[] for _ in range(len(data))]
for _ in range(num_classes)]
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
# append cfg.TEST.KEY_FRAME_INTERVAL padding images in the front (first frame)
while len(data_list) < cfg.TEST.KEY_FRAME_INTERVAL:
data_list.append(image)
feat_list.append(feat)
vis = False
file_idx = 0
thresh = 1e-3
for idx, element in enumerate(data):
data_batch = mx.io.DataBatch(data=[element], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, element)]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
if(idx != len(data)-1):
if len(data_list) < all_frame_interval - 1:
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
else:
#################################################
# main part of the loop
#################################################
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
pred_result = im_detect(aggr_predictors, data_batch, data_names, scales, cfg)
data_batch.data[0][-2] = None
data_batch.provide_data[0][-2] = ('data_cache', None)
data_batch.data[0][-1] = None
data_batch.provide_data[0][-1] = ('feat_cache', None)
out_im = process_pred_result(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
total_time = time.time()-t1
if (cfg.TEST.SEQ_NMS==False):
save_image(output_dir, file_idx, out_im)
print 'testing {} {:.4f}s'.format(str(file_idx)+'.JPEG', total_time /(file_idx+1))
file_idx += 1
else:
#################################################
# end part of a video #
#################################################
end_counter = 0
image, feat = get_resnet_output(feat_predictors, data_batch, data_names)
while end_counter < cfg.TEST.KEY_FRAME_INTERVAL + 1:
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
pred_result = im_detect(aggr_predictors, data_batch, data_names, scales, cfg)
out_im = process_pred_result(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
total_time = time.time() - t1
if (cfg.TEST.SEQ_NMS == False):
save_image(output_dir, file_idx, out_im)
print 'testing {} {:.4f}s'.format(str(file_idx)+'.JPEG', total_time / (file_idx+1))
file_idx += 1
end_counter+=1
if(cfg.TEST.SEQ_NMS):
video = [all_boxes[j][:] for j in range(1, num_classes)]
dets_all = seq_nms(video)
for cls_ind, dets_cls in enumerate(dets_all):
for frame_ind, dets in enumerate(dets_cls):
keep = nms(dets)
all_boxes[cls_ind + 1][frame_ind] = dets[keep, :]
for idx in range(len(data)):
boxes_this_image = [[]] + [all_boxes[j][idx] for j in range(1, num_classes)]
out_im = draw_all_detection(data[idx][0].asnumpy(), boxes_this_image, classes, scales[0], cfg)
save_image(output_dir, idx, out_im)
print 'done'
def main(tempFileList, fileOp):
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_rfcn_dcn'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
out_dir = os.path.join(
cur_path,
'demo/output/terror-det-rg-data-output/terror-det-v0.9-test/JPEGImages'
)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# set up class names
num_classes = 7
classes = [
'tibetan flag', 'guns', 'knives', 'not terror', 'islamic flag',
'isis flag'
]
# load demo data
image_names = tempFileList
data = []
for im_name in image_names:
im_file = im_name
print(im_file)
im = cv2.imread(im_file, cv2.IMREAD_COLOR)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/demo/models/' +
('rfcn_voc'),
10,
process=True)
#modify by zxt
#mx.model.save_checkpoint('f1/final', 10, sym, arg_params, aux_params)
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# warm up
for j in xrange(2):
data_batch = mx.io.DataBatch(data=[data[0]],
label=[],
pad=0,
index=0,
provide_data=[[
(k, v.shape)
for k, v in zip(data_names, data[0])
]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names,
scales, config)
# test
# fileOp = open(os.path.join(cur_path, 'terror-det-rg-test-result.txt'), 'w')
fileOp = fileOp
for idx, im_name in enumerate(image_names):
print("begining process %s" % (im_name))
data_batch = mx.io.DataBatch(
data=[data[idx]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
tic()
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names,
scales, config)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:,
4:8] if config.CLASS_AGNOSTIC else boxes[:, j *
4:(j +
1) *
4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
print 'testing {} {:.4f}s'.format(im_name, toc())
# visualize
im = cv2.imread(im_name)
#im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im_result = show_boxes(fileOp, im_name, im, dets_nms, classes, 1)
cv2.imwrite(out_dir + im_name.split('/')[-1], im_result)
print 'done'
def batch_extract(self, multiple=True, gt_dir=None, epoch=0):
"""
:param multiple:
:param gt_dir:
:return:
"""
if len(self.img_list) % self.batch_size != 0:
batch = len(self.img_list) / self.batch_size + 1
else:
batch = len(self.img_list) / self.batch_size
for i in xrange(batch):
if i < batch - 1:
self.batch_list = self.img_list[i * self.batch_size:(i + 1) *
self.batch_size]
else:
self.batch_list = self.img_list[i * self.batch_size:]
print '\nMini-batch %d\t' % (i + 1)
tmp_data = []
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
tic()
for img in self.batch_list:
assert os.path.exists(img), ('%s does not exist.'.format(img))
im = cv2.imread(
img, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
# im_info: height, width, scale
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
tmp_data.append({
self.data_names[0]: im_tensor,
self.data_names[1]: im_info
})
self.ctx = [int(i) for i in config.gpus.split(',')]
self.data = [[
mx.nd.array(tmp_data[i][name], mx.gpu(self.ctx[0]))
for name in self.data_names
] for i in xrange(len(tmp_data))]
max_data_shape = [[(self.data_names[0],
(1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape)
for k, v in zip(self.data_names, self.data[i])]
for i in xrange(len(self.data))]
provide_label = [None for i in xrange(len(self.data))]
arg_params, aux_params = load_param(self.model_dir,
epoch,
process=True)
self.predictor = Predictor(self.sym,
self.data_names,
self.label_name,
context=[mx.gpu(self.ctx[0])],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
print 'preparation: %.4fs' % toc()
if i == 0:
self.warmup()
self.forward(multiple=multiple, gt_dir=gt_dir)
self.cleaner()
def main():
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_deeplab_dcn' if not args.deeplab_only else 'resnet_v1_101_deeplab'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# set up class names
num_classes = 19
# load demo data
image_names = ['frankfurt_000001_073088_leftImg8bit.png', 'lindau_000024_000019_leftImg8bit.png']
data = []
for im_name in image_names:
assert os.path.exists(cur_path + '/../demo/' + im_name), ('%s does not exist'.format('../demo/' + im_name))
im = cv2.imread(cur_path + '/../demo/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data']
label_names = ['softmax_label']
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../model/' + ('deeplab_dcn_cityscapes' if not args.deeplab_only else 'deeplab_cityscapes'), 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
# warm up
for j in xrange(2):
data_batch = mx.io.DataBatch(data=[data[0]], label=[], pad=0, index=0,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[0])]],
provide_label=[None])
output_all = predictor.predict(data_batch)
output_all = [mx.ndarray.argmax(output['softmax_output'], axis=1).asnumpy() for output in output_all]
# test
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
tic()
output_all = predictor.predict(data_batch)
output_all = [mx.ndarray.argmax(output['softmax_output'], axis=1).asnumpy() for output in output_all]
pallete = getpallete(256)
segmentation_result = np.uint8(np.squeeze(output_all))
segmentation_result = Image.fromarray(segmentation_result)
segmentation_result.putpalette(pallete)
print 'testing {} {:.4f}s'.format(im_name, toc())
pure_im_name, ext_im_name = os.path.splitext(im_name)
segmentation_result.save(cur_path + '/../demo/seg_' + pure_im_name + '.png')
# visualize
im_raw = cv2.imread(cur_path + '/../demo/' + im_name)
seg_res = cv2.imread(cur_path + '/../demo/seg_' + pure_im_name + '.png')
cv2.imshow('Raw Image', im_raw)
cv2.imshow('segmentation_result', seg_res)
cv2.waitKey(0)
print 'done'
def im_detect_bbox_aug(net,
nms_wrapper,
img_path,
scales,
pixel_means,
bbox_stds,
ctx,
threshold=1e-3,
viz=False):
all_bboxes = []
all_scores = []
img_ori = cv2.imread(img_path.encode("utf-8"))
for scale_min, scale_max in scales:
fscale = 1.0 * scale_min / min(img_ori.shape[:2])
img_resized = cv2.resize(img_ori, (0, 0), fx=fscale, fy=fscale)
h, w, c = img_resized.shape
h_padded = h if h % 32 == 0 else h + 32 - h % 32
w_padded = w if w % 32 == 0 else w + 32 - w % 32
img_padded = np.zeros(shape=(h_padded, w_padded, c),
dtype=img_resized.dtype)
img_padded[:h, :w, :] = img_resized
img = transform(img_padded, pixel_means=pixel_means)
im_info = nd.array([[h_padded, w_padded, 1.0]], ctx=ctx[0])
data = nd.array(img, ctx=ctx[0])
rois, scores, bbox_deltas = net(data, im_info)
rois = rois[:, 1:].asnumpy()
bbox_deltas = bbox_deltas[0].asnumpy()
bbox_deltas = pre_compute_deltas(bbox_deltas, bbox_stds=bbox_stds)
bbox = bbox_pred(rois, bbox_deltas)
bbox = clip_boxes(bbox, data.shape[2:4])
bbox /= fscale
all_bboxes.append(bbox)
all_scores.append(scores[0].asnumpy())
# hflip
rois, scores, bbox_deltas = net(data[:, :, :, ::-1], im_info)
rois = rois[:, 1:].asnumpy()
bbox_deltas = bbox_deltas[0].asnumpy()
bbox_deltas = pre_compute_deltas(bbox_deltas, bbox_stds=bbox_stds)
bbox = bbox_pred(rois, bbox_deltas)
bbox = clip_boxes(bbox, data.shape[2:4])
tmp = bbox[:, 0::4].copy()
bbox[:, 0::4] = data.shape[3] - bbox[:, 2::4] - 1 # x0 = w - x0
bbox[:, 2::4] = data.shape[3] - tmp - 1 # x1 = w -x1
bbox /= fscale
all_bboxes.append(bbox)
all_scores.append(scores[0].asnumpy())
#
# vflip
rois, scores, bbox_deltas = net(data[:, :, ::-1], im_info)
rois = rois[:, 1:].asnumpy()
bbox_deltas = bbox_deltas[0].asnumpy()
bbox_deltas = pre_compute_deltas(bbox_deltas, bbox_stds=bbox_stds)
bbox = bbox_pred(rois, bbox_deltas)
bbox = clip_boxes(bbox, data.shape[2:4])
tmp = bbox[:, 1::4].copy()
bbox[:, 1::4] = data.shape[2] - bbox[:, 3::4] - 1 # x0 = w - x0
bbox[:, 3::4] = data.shape[2] - tmp - 1 # x1 = w -x1
bbox /= fscale
all_bboxes.append(bbox)
all_scores.append(scores[0].asnumpy())
# vhflip
rois, scores, bbox_deltas = net(data[:, :, ::-1, ::-1], im_info)
rois = rois[:, 1:].asnumpy()
bbox_deltas = bbox_deltas[0].asnumpy()
bbox_deltas = pre_compute_deltas(bbox_deltas, bbox_stds=bbox_stds)
bbox = bbox_pred(rois, bbox_deltas)
bbox = clip_boxes(bbox, data.shape[2:4])
tmp = bbox[:, 1::4].copy()
bbox[:, 1::4] = data.shape[2] - bbox[:, 3::4] - 1 # x0 = w - x0
bbox[:, 3::4] = data.shape[2] - tmp - 1 # x1 = w -x1
tmp = bbox[:, 0::4].copy()
bbox[:, 0::4] = data.shape[3] - bbox[:, 2::4] - 1 # x0 = w - x0
bbox[:, 2::4] = data.shape[3] - tmp - 1 # x1 = w -x1
bbox /= fscale
all_bboxes.append(bbox)
all_scores.append(scores[0].asnumpy())
all_bboxes = np.concatenate(all_bboxes, axis=0)
all_scores = np.concatenate(all_scores, axis=0)
pred_bboxes = []
pred_scores = []
pred_clsid = []
for j in range(1, all_scores.shape[1]):
cls_scores = all_scores[:, j, np.newaxis]
cls_boxes = all_bboxes[:, 4:
8] if config.CLASS_AGNOSTIC else all_bboxes[:,
j *
4:
(j +
1
) *
4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms_wrapper(cls_dets.astype('f'))
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > threshold, :]
pred_bboxes.append(cls_dets[:, :4])
pred_scores.append(cls_dets[:, 4])
pred_clsid.append(j *
np.ones(shape=(cls_dets.shape[0], ), dtype=np.int))
pred_bboxes = np.concatenate(pred_bboxes, axis=0)
pred_scores = np.concatenate(pred_scores, axis=0)
pred_clsid = np.concatenate(pred_clsid, axis=0)
if viz:
import gluoncv
import matplotlib.pyplot as plt
gluoncv.utils.viz.plot_bbox(img_ori[:, :, ::-1],
bboxes=pred_bboxes,
scores=pred_scores,
labels=pred_clsid,
thresh=.5)
plt.show()
return pred_bboxes, pred_scores, pred_clsid
def predict(self, images, feat_output, aggr_feat_output):
model = self.model
all_frame_interval = self.all_frame_interval
feat_sym = self.feat_sym
aggr_sym = self.aggr_sym
num_classes = self.num_classes
classes = self.classes
max_per_image = self.max_per_image
output_dir = cur_path + '/../demo/rfcn_fgfa_{}/'.format(self.index)
self.index += 1
if not os.path.exists(output_dir):
os.makedirs(output_dir)
data = []
for im in images:
target_size = cfg.SCALES[0][0]
max_size = cfg.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=cfg.network.IMAGE_STRIDE)
im_tensor = transform(im, cfg.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
feat_stride = float(cfg.network.RCNN_FEAT_STRIDE)
data.append({
'data': im_tensor,
'im_info': im_info,
'data_cache': im_tensor,
'feat_cache': im_tensor
})
# get predictor
print 'get-predictor'
data_names = ['data', 'im_info', 'data_cache', 'feat_cache']
label_names = []
t1 = time.time()
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[
('data', (1, 3, max([v[0] for v in cfg.SCALES]),
max([v[1] for v in cfg.SCALES]))),
('data_cache', (11, 3, max([v[0] for v in cfg.SCALES]),
max([v[1] for v in cfg.SCALES]))),
('feat_cache',
((11, cfg.network.FGFA_FEAT_DIM,
np.ceil(max([v[0] for v in cfg.SCALES]) / feat_stride).astype(
np.int),
np.ceil(max([v[1] for v in cfg.SCALES]) / feat_stride).astype(
np.int))))
]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for _ in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + model, 0, process=True)
feat_predictors = Predictor(feat_sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
aggr_predictors = Predictor(aggr_sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = py_nms_wrapper(cfg.TEST.NMS)
# First frame of the video
idx = 0
data_batch = mx.io.DataBatch(
data=[data[idx]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
all_boxes = [[[] for _ in range(len(data))]
for _ in range(num_classes)]
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
# append cfg.TEST.KEY_FRAME_INTERVAL padding images in the front (first frame)
while len(data_list) < cfg.TEST.KEY_FRAME_INTERVAL:
data_list.append(image)
feat_list.append(feat)
vis = False
file_idx = 0
thresh = 1e-3
for idx, element in enumerate(data):
data_batch = mx.io.DataBatch(
data=[element],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, element)]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
if (idx != len(data) - 1):
if len(data_list) < all_frame_interval - 1:
image, feat = get_resnet_output(feat_predictors,
data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
else:
#################################################
# main part of the loop
#################################################
image, feat = get_resnet_output(feat_predictors,
data_batch, data_names)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
pred_result, aggr_feat = im_detect(aggr_predictors,
data_batch, data_names,
scales, cfg)
assert len(aggr_feat) == 1
data_batch.data[0][-2] = None
data_batch.provide_data[0][-2] = ('data_cache', None)
data_batch.data[0][-1] = None
data_batch.provide_data[0][-1] = ('feat_cache', None)
out_im = process_pred_result(
classes, pred_result, num_classes, thresh, cfg, nms,
all_boxes, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
total_time = time.time() - t1
if (cfg.TEST.SEQ_NMS == False):
save_image(output_dir, file_idx, out_im)
print 'testing {} {:.4f}s'.format(
str(file_idx) + '.JPEG', total_time / (file_idx + 1))
file_idx += 1
else:
#################################################
# end part of a video #
#################################################
end_counter = 0
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
while end_counter < cfg.TEST.KEY_FRAME_INTERVAL + 1:
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
pred_result, aggr_feat = im_detect(aggr_predictors,
data_batch, data_names,
scales, cfg)
assert len(aggr_feat) == 1
out_im = process_pred_result(
classes, pred_result, num_classes, thresh, cfg, nms,
all_boxes, file_idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
total_time = time.time() - t1
if (cfg.TEST.SEQ_NMS == False):
save_image(output_dir, file_idx, out_im)
print 'testing {} {:.4f}s'.format(
str(file_idx) + '.JPEG', total_time / (file_idx + 1))
file_idx += 1
end_counter += 1
if (cfg.TEST.SEQ_NMS):
video = [all_boxes[j][:] for j in range(1, num_classes)]
dets_all = seq_nms(video)
for cls_ind, dets_cls in enumerate(dets_all):
for frame_ind, dets in enumerate(dets_cls):
keep = nms(dets)
all_boxes[cls_ind + 1][frame_ind] = dets[keep, :]
for idx in range(len(data)):
boxes_this_image = [[]] + [
all_boxes[j][idx] for j in range(1, num_classes)
]
out_im = draw_all_detection(data[idx][0].asnumpy(),
boxes_this_image, classes,
scales[0], cfg)
save_image(output_dir, idx, out_im)
print 'done'
def main():
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_rfcn'
model = '/../model/rfcn_vid'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_test_symbol(config)
# set up class names
num_classes = 31
classes = ['airplane', 'antelope', 'bear', 'bicycle',
'bird', 'bus', 'car', 'cattle',
'dog', 'domestic_cat', 'elephant', 'fox',
'giant_panda', 'hamster', 'horse', 'lion',
'lizard', 'monkey', 'motorcycle', 'rabbit',
'red_panda', 'sheep', 'snake', 'squirrel',
'tiger', 'train', 'turtle', 'watercraft',
'whale', 'zebra']
# load demo data
image_names = glob.glob(cur_path + '/../demo/ILSVRC2015_val_00007010/*.JPEG')
output_dir = cur_path + '/../demo/rfcn/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
#
data = []
for im_name in image_names:
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
im, im_scale = resize(im, target_size, max_size, stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array([[im_tensor.shape[2], im_tensor.shape[3], im_scale]], dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names] for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])] for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + model, 0, process=True)
predictor = Predictor(sym, data_names, label_names,
context=[mx.gpu(0)], max_data_shapes=max_data_shape,
provide_data=provide_data, provide_label=provide_label,
arg_params=arg_params, aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# warm up
for j in xrange(2):
data_batch = mx.io.DataBatch(data=[data[0]], label=[], pad=0, index=0,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[0])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
# test
time = 0
count = 0
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(data=[data[idx]], label=[], pad=0, index=idx,
provide_data=[[(k, v.shape) for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
tic()
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names, scales, config)
time += toc()
count += 1
print 'testing {} {:.4f}s'.format(im_name, time/count)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:, 4:8] if config.CLASS_AGNOSTIC else boxes[:, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
# visualize
im = cv2.imread(im_name)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# show_boxes(im, dets_nms, classes, 1)
out_im = draw_boxes(im, dets_nms, classes, 1)
_, filename = os.path.split(im_name)
cv2.imwrite(output_dir + filename,out_im)
print 'done'
def main():
# get symbol
pprint.pprint(config)
config.symbol = 'resnet_v1_101_rfcn'
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# set up class names; Don't count the background in, even we are treat the background as label '0'
num_classes = 4
classes = ['vehicle', 'pedestrian', 'cyclist', 'traffic lights']
# load demo data
image_path = './data/RoadImages/test/'
image_names = glob.glob(image_path + '*.jpg')
print("Image amount {}".format(len(image_names)))
data = []
for im_name in image_names:
assert os.path.exists(im_name), ('%s does not exist'.format(im_name))
im = cv2.imread(im_name,
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
target_size = config.SCALES[0][1]
max_size = config.SCALES[0][1]
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
im_tensor = transform(im, config.network.PIXEL_MEANS)
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(
'./output/rfcn/road_obj/road_train_all/all/' + 'rfcn_road',
19,
process=True)
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(0)],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
nms = gpu_nms_wrapper(config.TEST.NMS, 0)
# test
notation_dict = {}
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(
data=[data[idx]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
tic()
scores, boxes, data_dict = im_detect(predictor, data_batch, data_names,
scales, config)
boxes = boxes[0].astype('f')
scores = scores[0].astype('f')
dets_nms = []
for j in range(1, scores.shape[1]):
cls_scores = scores[:, j, np.newaxis]
cls_boxes = boxes[:,
4:8] if config.CLASS_AGNOSTIC else boxes[:, j *
4:(j +
1) *
4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
cls_dets = cls_dets[keep, :]
cls_dets = cls_dets[cls_dets[:, -1] > 0.7, :]
dets_nms.append(cls_dets)
print 'testing {} {:.4f}s'.format(im_name, toc())
# notation_list.append(get_notation(im_name, dets_nms, classes, scale=1.0, gen_bbox_pic=True))
notation_dict.update(
get_notation(im_name,
dets_nms,
classes,
scale=1.0,
gen_bbox_pic=True))
save_notation_file(notation_dict)
print 'done'
def main():
# get symbol
ctx_id = [int(i) for i in config.gpus.split(',')]
pprint.pprint(config)
sym_instance = eval(config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
# set up class names
# set up class names
num_classes = 81
classes = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
# load demo data
image_names = []
names_dirs = os.listdir(cur_path + '/../' + test_dir)
for im_name in names_dirs:
if im_name[-4:] == '.jpg' or im_name[-4:] == '.png':
image_names.append(im_name)
data = []
for im_name in image_names:
assert os.path.exists(cur_path + '/../' + test_dir + im_name), (
'%s does not exist'.format('../demo/' + im_name))
im = cv2.imread(cur_path + '/../' + test_dir + im_name,
cv2.IMREAD_COLOR | long(128))
target_size = config.SCALES[0][0]
max_size = config.SCALES[0][1]
#print "before scale: "
#print im.shape
im, im_scale = resize(im,
target_size,
max_size,
stride=config.network.IMAGE_STRIDE)
#print "after scale: "
#print im.shape
#im_scale = 1.0
#print "scale ratio: "
#print im_scale
im_tensor = transform(im, config.network.PIXEL_MEANS)
#print im_tensor.shape
im_info = np.array(
[[im_tensor.shape[2], im_tensor.shape[3], im_scale]],
dtype=np.float32)
data.append({'data': im_tensor, 'im_info': im_info})
# get predictor
data_names = ['data', 'im_info']
label_names = []
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in xrange(len(data))]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]]
provide_data = [[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in xrange(len(data))]
provide_label = [None for i in xrange(len(data))]
arg_params, aux_params = load_param(cur_path + '/../' + model_dir,
0,
process=True)
predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(ctx_id[0])],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
# warm up
for i in xrange(2):
data_batch = mx.io.DataBatch(data=[data[0]],
label=[],
pad=0,
index=0,
provide_data=[[
(k, v.shape)
for k, v in zip(data_names, data[0])
]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
_, _, _, _ = im_detect(predictor, data_batch, data_names, scales,
config)
# test
for idx, im_name in enumerate(image_names):
data_batch = mx.io.DataBatch(
data=[data[idx]],
label=[],
pad=0,
index=idx,
provide_data=[[(k, v.shape)
for k, v in zip(data_names, data[idx])]],
provide_label=[None])
scales = [
data_batch.data[i][1].asnumpy()[0, 2]
for i in xrange(len(data_batch.data))
]
tic()
scores, boxes, masks, data_dict = im_detect(predictor, data_batch,
data_names, [1.0], config)
im_shapes = [
data_batch.data[i][0].shape[2:4]
for i in xrange(len(data_batch.data))
]
#print im_shapes
if not config.TEST.USE_MASK_MERGE:
all_boxes = [[] for _ in xrange(num_classes)]
all_masks = [[] for _ in xrange(num_classes)]
nms = py_nms_wrapper(config.TEST.NMS)
for j in range(1, num_classes):
indexes = np.where(scores[0][:, j] > 0.7)[0]
cls_scores = scores[0][indexes, j, np.newaxis]
cls_masks = masks[0][indexes, 1, :, :]
try:
if config.CLASS_AGNOSTIC:
cls_boxes = boxes[0][indexes, :]
else:
raise Exception()
except:
cls_boxes = boxes[0][indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
all_boxes[j] = cls_dets[keep, :]
all_masks[j] = cls_masks[keep, :]
dets = [all_boxes[j] for j in range(1, num_classes)]
masks = [all_masks[j] for j in range(1, num_classes)]
else:
masks = masks[0][:, 1:, :, :]
result_masks, result_dets = gpu_mask_voting(
masks, boxes[0], scores[0], num_classes, 100, im_shapes[0][1],
im_shapes[0][0], config.TEST.NMS,
config.TEST.MASK_MERGE_THRESH, config.BINARY_THRESH, ctx_id[0])
dets = [result_dets[j] for j in range(1, num_classes)]
masks = [
result_masks[j][:, 0, :, :] for j in range(1, num_classes)
]
print 'testing {} {:.4f}s'.format(im_name, toc())
# visualize
for i in xrange(len(dets)):
keep = np.where(dets[i][:, -1] > 0.7)
dets[i] = dets[i][keep]
masks[i] = masks[i][keep]
im = cv2.imread(cur_path + '/../' + test_dir + im_name)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
show_masks(im, dets, masks, classes, config, 1.0 / scales[0], False)
# Save img
cv2.imwrite(cur_path + '/../' + result_dir + im_name,
cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
print 'done'
