def batch_detection(network,
images,
class_names,
class_colors,
thresh=0.25,
hier_thresh=.5,
nms=.45,
batch_size=4):
image_height, image_width, _ = check_batch_shape(images, batch_size)
darknet_images = prepare_batch(images, network)
batch_detections = darknet.network_predict_batch(network, darknet_images,
batch_size, image_width,
image_height, thresh,
hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names, num)
images[idx] = darknet.draw_boxes(predictions, images[idx],
class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
return images, batch_predictions
def run_detection(output_splits, args):
# Determine an optimal batch size
if args.batch_size == None:
args.batch_size = 1
n_images = len(output_splits)
while (n_images % (2 * args.batch_size) == 0):
args.batch_size *= 2
n_batches = int(n_images / args.batch_size)
print('Optimal batch size: {}'.format(args.batch_size))
print('Loading network.')
# Load network
random.seed(3) # Deterministic bbox colours
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=args.batch_size)
# Get network size
n_w = darknet.network_width(network)
n_h = darknet.network_height(network)
# Split in batches
global_predictions = []
drawn_images = []
for batch in range(n_batches):
prev_time = time.time() # Get batch calculation start time
# Get images in the batch
names_batch = output_splits[batch * args.batch_size:((batch + 1) *
args.batch_size)]
images_batch = [cv2.imread(n) for n in names_batch]
images_batch = [
cv2.resize(n, (n_w, n_h), interpolation=cv2.INTER_LINEAR)
for n in images_batch
] # Ensure image resolution is the same as network's
h, w, _ = check_batch_shape(images_batch, args.batch_size)
dnet_images = darknet_images.prepare_batch(images_batch, network)
# Detect
batch_detections = darknet.network_predict_batch(
network, dnet_images, args.batch_size, w, h, args.thresh,
args.hier_thresh, None, 0, 0)
# Process detections
batch_predictions = []
for idx in range(args.batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if args.nms:
darknet.do_nms_obj(detections, num, len(class_names), args.nms)
predictions = darknet.remove_negatives(detections, class_names,
num)
images_batch[idx] = darknet.draw_boxes(predictions,
images_batch[idx],
class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, args.batch_size)
# Store predictions and drawn images
global_predictions.extend(batch_predictions)
images_batch = [
cv2.cvtColor(n, cv2.COLOR_BGR2RGB) for n in images_batch
] # Ensure output is in RGB
drawn_images.extend(images_batch)
# Calculate FPS
fps = int(args.batch_size / (time.time() - prev_time))
print("FPS: {}".format(fps))
return drawn_images, global_predictions, class_names
break
arr = numpy.frombuffer(buf, dtype='uint8').reshape(
(batch_size, height, width, 3))
arr = arr.transpose((0, 3, 1, 2))
arr = numpy.ascontiguousarray(arr.flat, dtype='float32') / 255.0
darknet_images = arr.ctypes.data_as(darknet.POINTER(darknet.c_float))
darknet_images = darknet.IMAGE(width, height, 3, darknet_images)
raw_detections = darknet.network_predict_batch(net, darknet_images,
batch_size, width, height,
threshold, 0.5, None, 0, 0)
detections = []
for idx in range(batch_size):
num = raw_detections[idx].num
raw_dlist = raw_detections[idx].dets
darknet.do_nms_obj(raw_dlist, num, len(class_names), 0.45)
raw_dlist = darknet.remove_negatives(raw_dlist, class_names, num)
dlist = []
for cls, score, (cx, cy, w, h) in raw_dlist:
dlist.append({
'Category': cls,
'Score': float(score),
'Left': int(cx - w / 2),
'Right': int(cx + w / 2),
'Top': int(cy - h / 2),
'Bottom': int(cy + h / 2),
})
detections.append(dlist)
darknet.free_batch_detections(raw_detections, batch_size)
print('json' + json.dumps(detections), flush=True)
def darknet_batch_detection_rs_images(network,
image_path,
save_dir,
patch_groups,
patch_count,
class_names,
batch_size,
thresh=0.25,
hier_thresh=.5,
nms=.45):
'''
# run batch detection of YOLO on an remote sensing image.
:param network: a darknet network (already load the weight)
:param image_path: the image patch.
:param patch_groups: the group of images patch, each group has the same width and height.
:param class_names:
:param batch_size:
:param thresh:
:param hier_thresh:
:param nms:
:return:
'''
# read the entire image
entire_img_data, nodata = raster_io.read_raster_all_bands_np(image_path)
entire_img_data = entire_img_data.transpose(1, 2, 0) # to opencv format
# # RGB to BGR: Matplotlib image to OpenCV https://www.scivision.dev/numpy-image-bgr-to-rgb/
# entire_img_data = entire_img_data[..., ::-1].copy() # cancel RGB to BGR, since it make results worse, (maybe darknet already read images as RGB during training)
entire_height, entire_width, band_num = entire_img_data.shape
print("entire_height, entire_width, band_num", entire_height, entire_width,
band_num)
if band_num not in [1, 3]:
raise ValueError('only accept one band or three band images')
# class_colors={} #{'thaw_slump': (139, 140, 228)} # class_colors should get when load network
# for name in class_names:
# class_colors[name] = (0,0,0)
patch_idx = 0
for key in patch_groups.keys():
patches_sameSize = patch_groups[key]
batch_patches = [
patches_sameSize[i * batch_size:(i + 1) * batch_size]
for i in range((len(patches_sameSize) + batch_size - 1) //
batch_size)
]
for a_batch_patch in batch_patches:
t0 = time.time()
input_batch_size = len(a_batch_patch)
if input_batch_size != batch_size:
if input_batch_size > batch_size:
raise ValueError(
'input_batch_size (%d) is greater than batch_size (%d)'
% (input_batch_size, batch_size))
while (len(a_batch_patch) < batch_size):
a_batch_patch.append(
a_batch_patch[0]
) # copy the first one to fit the batch size
images = [
copy_one_patch_image_data(patch, entire_img_data)
for patch in a_batch_patch
]
# for the batch prediction, it seems have to resize to the network size
img_height, img_width, band_num = images[0].shape
width = darknet.network_width(network)
height = darknet.network_height(network)
# darknet_images = prepare_batch(images, network)
# prepare_batch
darknet_images = []
for image in images:
# print('image shape',image.shape)
image_resized = cv2.resize(image, (width, height),
interpolation=cv2.INTER_LINEAR)
if band_num == 1:
image_resized = np.expand_dims(
image_resized, axis=2) # add one diminsion at the last
# print('image_resized shape', image_resized.shape)
custom_image = image_resized.transpose(2, 0, 1)
darknet_images.append(custom_image)
batch_array = np.concatenate(darknet_images, axis=0)
batch_array = np.ascontiguousarray(batch_array.flat,
dtype=np.float32) / 255.0
darknet_images = batch_array.ctypes.data_as(
darknet.POINTER(darknet.c_float))
darknet_images = darknet.IMAGE(width, height, band_num,
darknet_images)
# prediction
batch_detections = darknet.network_predict_batch(
network, darknet_images, batch_size, img_height, img_width,
thresh, hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(input_batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names,
num)
# images[idx] = np.ascontiguousarray(images[idx])
# images[idx] = darknet.draw_boxes(predictions, images[idx], class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
for idx, (patch, image, predictions) in enumerate(
zip(a_batch_patch, images, batch_predictions)):
# remove the duplicated ones
if idx >= input_batch_size:
break
# save results
save_res_json = os.path.join(save_dir, '%d.json' % patch_idx)
# the confidence output by network_predict_batch is 0-1, convert to percent
save_one_patch_detection_json(patch,
predictions,
class_names,
save_res_json,
b_percent=True)
# cv2.imwrite('%d.png'%patch_idx, image) # save for testing
# for label, confidence, bbox in predictions:
# bbox = darknet.bbox2points(bbox) # to [xmin, ymin, xmax, ymax]
# print(label, class_names.index(label), bbox, confidence)
if patch_idx % 100 == 0:
print('saving %d patch, total: %d, cost %f second' %
(patch_idx, patch_count,
(time.time() - t0) / batch_size))
patch_idx += 1
