Python utils.image.resize() Examples
The following are 7
code examples of utils.image.resize().
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Example #1
| Source File: batch_generator.py From BirdCLEF-Baseline with MIT License | 6 votes |
|
def loadImageAndTarget(sample, augmentation):
# Load image
img = image.openImage(sample[0], cfg.IM_DIM)
# Resize Image
img = image.resize(img, cfg.IM_SIZE[0], cfg.IM_SIZE[1], mode=cfg.RESIZE_MODE)
# Do image Augmentation
if augmentation:
img = image.augment(img, cfg.IM_AUGMENTATION, cfg.AUGMENTATION_COUNT, cfg.AUGMENTATION_PROBABILITY)
# Prepare image for net input
img = image.normalize(img, cfg.ZERO_CENTERED_NORMALIZATION)
img = image.prepare(img)
# Get target
label = sample[1]
index = cfg.CLASSES.index(label)
target = np.zeros((len(cfg.CLASSES)), dtype='float32')
target[index] = 1.0
return img, target
#################### BATCH HANDLING #####################
Example #2
| Source File: submission_soundscape.py From BirdCLEF-Baseline with MIT License | 4 votes |
|
def getSpecBatches(split):
# Random Seed
random = cfg.getRandomState()
# Make predictions for every testfile
for t in split:
# Spec batch
spec_batch = []
# Keep track of timestamps
pred_start = 0
# Get specs for file
for spec in audio.specsFromFile(t[0],
cfg.SAMPLE_RATE,
cfg.SPEC_LENGTH,
cfg.SPEC_OVERLAP,
cfg.SPEC_MINLEN,
shape=(cfg.IM_SIZE[1], cfg.IM_SIZE[0]),
fmin=cfg.SPEC_FMIN,
fmax=cfg.SPEC_FMAX):
# Resize spec
spec = image.resize(spec, cfg.IM_SIZE[0], cfg.IM_SIZE[1], mode=cfg.RESIZE_MODE)
# Normalize spec
spec = image.normalize(spec, cfg.ZERO_CENTERED_NORMALIZATION)
# Prepare as input
spec = image.prepare(spec)
# Add to batch
if len(spec_batch) > 0:
spec_batch = np.vstack((spec_batch, spec))
else:
spec_batch = spec
# Batch too large?
if spec_batch.shape[0] >= cfg.MAX_SPECS_PER_FILE:
break
# Do we have enough specs for a prediction?
if len(spec_batch) >= cfg.SPECS_PER_PREDICTION:
# Calculate next timestamp
pred_end = pred_start + cfg.SPEC_LENGTH + ((len(spec_batch) - 1) * (cfg.SPEC_LENGTH - cfg.SPEC_OVERLAP))
# Store prediction
ts = getTimestamp(int(pred_start), int(pred_end))
# Advance to next timestamp
pred_start = pred_end - cfg.SPEC_OVERLAP
yield spec_batch, t[1], ts, t[0].split(os.sep)[-1]
# Spec batch
spec_batch = []
Example #3
| Source File: test.py From BirdCLEF-Baseline with MIT License | 4 votes |
|
def getSpecBatches(split):
# Random Seed
random = cfg.getRandomState()
# Make predictions for every testfile
for t in split:
# Spec batch
spec_batch = []
# Get specs for file
for spec in audio.specsFromFile(t[0],
cfg.SAMPLE_RATE,
cfg.SPEC_LENGTH,
cfg.SPEC_OVERLAP,
cfg.SPEC_MINLEN,
shape=(cfg.IM_SIZE[1], cfg.IM_SIZE[0]),
fmin=cfg.SPEC_FMIN,
fmax=cfg.SPEC_FMAX,
spec_type=cfg.SPEC_TYPE):
# Resize spec
spec = image.resize(spec, cfg.IM_SIZE[0], cfg.IM_SIZE[1], mode=cfg.RESIZE_MODE)
# Normalize spec
spec = image.normalize(spec, cfg.ZERO_CENTERED_NORMALIZATION)
# Prepare as input
spec = image.prepare(spec)
# Add to batch
if len(spec_batch) > 0:
spec_batch = np.vstack((spec_batch, spec))
else:
spec_batch = spec
# Batch too large?
if spec_batch.shape[0] >= cfg.MAX_SPECS_PER_FILE:
break
# No specs?
if len(spec_batch) == 0:
spec = random.normal(0.0, 1.0, (cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
spec_batch = image.prepare(spec)
# Shuffle spec batch
spec_batch = shuffle(spec_batch, random_state=random)
# yield batch, labels and filename
yield spec_batch[:cfg.MAX_SPECS_PER_FILE], t[1], t[0].split(os.sep)[-1]
Example #4
| Source File: deform_conv_demo.py From kaggle-rsna18 with MIT License | 4 votes |
|
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])
Example #5
| Source File: deform_psroi_demo.py From kaggle-rsna18 with MIT License | 4 votes |
|
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])
Example #6
| Source File: deform_conv_demo.py From Deformable-ConvNets with MIT License | 4 votes |
|
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])
Example #7
| Source File: deform_psroi_demo.py From Deformable-ConvNets with MIT License | 4 votes |
|
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])
