def eval_and_plot_faster_rcnn(eval_model, num_images_to_plot, test_map_file, img_shape,
results_base_path, feature_node_name, classes,
drawUnregressedRois=False, drawNegativeRois=False,
nmsThreshold=0.5, nmsConfThreshold=0.0, bgrPlotThreshold = 0.8):
# get image paths
with open(test_map_file) as f:
content = f.readlines()
img_base_path = os.path.dirname(os.path.abspath(test_map_file))
img_file_names = [os.path.join(img_base_path, x.split('\t')[1]) for x in content]
# prepare model
image_input = input_variable(img_shape, dynamic_axes=[Axis.default_batch_axis()], name=feature_node_name)
dims_input = input_variable((1,6), dynamic_axes=[Axis.default_batch_axis()], name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
#dims_input_const = cntk.constant([image_width, image_height, image_width, image_height, image_width, image_height], (1, 6))
print("Plotting results from Faster R-CNN model for %s images." % num_images_to_plot)
for i in range(0, num_images_to_plot):
imgPath = img_file_names[i]
# evaluate single image
_, cntk_img_input, dims = load_resize_and_pad(imgPath, img_shape[2], img_shape[1])
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1,) + dims_input.shape
output = frcn_eval.eval({frcn_eval.arguments[0]: [cntk_img_input], frcn_eval.arguments[1]: dims_input})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
if drawUnregressedRois:
# plot results without final regression
imgDebug = visualizeResultsFaster(imgPath, labels, scores, out_rpn_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=None, boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_{}".format(results_base_path, i, os.path.basename(imgPath)), imgDebug)
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels, dims)
nmsKeepIndices = apply_nms_to_single_image_results(regressed_rois, labels, scores,
nms_threshold=nmsThreshold,
conf_threshold=nmsConfThreshold)
img = visualizeResultsFaster(imgPath, labels, scores, regressed_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=nmsKeepIndices,
boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_regr_{}".format(results_base_path, i, os.path.basename(imgPath)), img)
def eval_and_plot_faster_rcnn(eval_model, num_images_to_plot, test_map_file, img_shape,
results_base_path, feature_node_name, classes,
drawUnregressedRois=False, drawNegativeRois=False,
nmsThreshold=0.5, nmsConfThreshold=0.0, bgrPlotThreshold = 0.8):
# get image paths
with open(test_map_file) as f:
content = f.readlines()
img_base_path = os.path.dirname(os.path.abspath(test_map_file))
img_file_names = [os.path.join(img_base_path, x.split('\t')[1]) for x in content]
# prepare model
image_input = input_variable(img_shape, dynamic_axes=[Axis.default_batch_axis()], name=feature_node_name)
dims_input = input_variable((1,6), dynamic_axes=[Axis.default_batch_axis()], name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
#dims_input_const = cntk.constant([image_width, image_height, image_width, image_height, image_width, image_height], (1, 6))
print("Plotting results from Faster R-CNN model for %s images." % num_images_to_plot)
for i in range(0, num_images_to_plot):
imgPath = img_file_names[i]
# evaluate single image
_, cntk_img_input, dims = load_resize_and_pad(imgPath, img_shape[2], img_shape[1])
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1,) + dims_input.shape
output = frcn_eval.eval({frcn_eval.arguments[0]: [cntk_img_input], frcn_eval.arguments[1]: dims_input})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
if drawUnregressedRois:
# plot results without final regression
imgDebug = visualizeResultsFaster(imgPath, labels, scores, out_rpn_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=None, boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_{}".format(results_base_path, i, os.path.basename(imgPath)), imgDebug)
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels, dims)
nmsKeepIndices = apply_nms_to_single_image_results(regressed_rois, labels, scores,
nms_threshold=nmsThreshold,
conf_threshold=nmsConfThreshold)
img = visualizeResultsFaster(imgPath, labels, scores, regressed_rois, img_shape[2], img_shape[1],
classes, nmsKeepIndices=nmsKeepIndices,
boDrawNegativeRois=drawNegativeRois,
decisionThreshold=bgrPlotThreshold)
imsave("{}/{}_regr_{}".format(results_base_path, i, os.path.basename(imgPath)), img)
def faster_rcnn_pred(eval_model,
prediction_in,
prediction_out,
class_map_file,
img_shape,
feature_node_name,
nms_threshold=0.5,
nms_conf_threshold=0.0,
headers=True,
output_width_height=False,
suppressed_labels=()):
"""
Performs predictions with a model in continuous mode.
:param eval_model: the model to use
:param prediction_in: the input dir for images (list)
:param prediction_out: the output dir for images and ROI CSV files (list)
:param class_map_file: the class map file to use
:param img_shape: the shpage
:param feature_node_name: the name of the feature node
:param nms_threshold:
:param nms_conf_threshold:
:param headers: whether to output the headers in the ROI file
:param output_width_height: whether to output x/y/width/height or x0/y0/x1/y1
:param suppressed_labels: the labels to suppress from being output in the ROI files
"""
# load labels
str_labels = load_class_labels(class_map_file)
print("Class labels: %s" % str(str_labels))
# prepare model
image_input = input_variable(img_shape,
dynamic_axes=[Axis.default_batch_axis()],
name=feature_node_name)
dims_input = input_variable((1, 6),
dynamic_axes=[Axis.default_batch_axis()],
name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
while True:
any = False
# iterate directory pairs
for p in range(len(prediction_in)):
pred_in = prediction_in[p]
pred_out = prediction_out[p]
# only png and jpg files
files = [
(pred_in + os.sep + x) for x in os.listdir(pred_in)
if (x.lower().endswith(".png") or x.lower().endswith(".jpg"))
]
for f in files:
start = datetime.now()
print(start, "-", f)
img_path = pred_out + os.sep + os.path.basename(f)
roi_path = pred_out + os.sep + os.path.splitext(
os.path.basename(f))[0] + ".csv"
cntk_img_input = None
try:
_, cntk_img_input, dims = load_resize_and_pad(
f, img_shape[2], img_shape[1])
except Exception as e:
print(str(e))
try:
# delete any existing old files in output dir
if os.path.exists(img_path):
try:
os.remove(img_path)
except:
print(
"Failed to remove existing image in output directory: ",
img_path)
if os.path.exists(roi_path):
try:
os.remove(roi_path)
except:
print(
"Failed to remove existing ROI file in output directory: ",
roi_path)
# move into output dir
os.rename(f, img_path)
except:
img_path = None
if cntk_img_input is None:
continue
if img_path is None:
continue
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1, ) + dims_input.shape
output = frcn_eval.eval({
frcn_eval.arguments[0]: [cntk_img_input],
frcn_eval.arguments[1]:
dims_input
})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr,
labels, dims)
nms_keep_indices = apply_nms_to_single_image_results(
regressed_rois,
labels,
scores,
nms_threshold=nms_threshold,
conf_threshold=nms_conf_threshold)
save_rois_to_file(regressed_rois,
nms_keep_indices,
labels,
str_labels,
scores,
pred_out,
img_path,
headers=headers,
output_width_height=output_width_height,
suppressed_labels=suppressed_labels,
dims=dims)
timediff = datetime.now() - start
print(" time:", timediff)
# nothing processed at all, lets wait for files to appear
if not any:
sleep(1)
def faster_rcnn_eval(eval_model,
test_map_file,
class_map_file,
img_shape,
results_base_path,
feature_node_name,
nms_threshold=0.5,
nms_conf_threshold=0.0,
headers=True,
output_width_height=False,
suppressed_labels=()):
"""
Tests a Faster R-CNN model and outputs rois.
:param eval_model: the model
:param test_map_file: the map file with the test images/labels
:param class_map_file: the class map file
:param img_shape: the shape
:param results_base_path: the base directory for the results
:param feature_node_name: the feature node name
:param nms_threshold:
:param nms_conf_threshold:
:param headers: whether to output the headers in the ROI file
:param output_width_height: whether to output x/y/width/height or x0/y0/x1/y1
:param suppressed_labels: the labels to suppress from being output in the ROI files
"""
# load labels
str_labels = load_class_labels(class_map_file)
print("Class labels: %s" % str(str_labels))
# get image paths
with open(test_map_file) as f:
content = f.readlines()
img_base_path = os.path.dirname(os.path.abspath(test_map_file))
img_file_names = [
os.path.join(img_base_path,
x.split('\t')[1]) for x in content
]
# prepare model
image_input = input_variable(img_shape,
dynamic_axes=[Axis.default_batch_axis()],
name=feature_node_name)
dims_input = input_variable((1, 6),
dynamic_axes=[Axis.default_batch_axis()],
name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
print("Outputting results from Faster R-CNN model for %s images." %
len(img_file_names))
for i in range(0, len(img_file_names)):
start = datetime.now()
img_path = img_file_names[i]
print(
str(i + 1) + "/" + str(len(img_file_names)) + ": " +
img_file_names[i])
# evaluate single image
_, cntk_img_input, dims = load_resize_and_pad(img_path, img_shape[2],
img_shape[1])
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1, ) + dims_input.shape
output = frcn_eval.eval({
frcn_eval.arguments[0]: [cntk_img_input],
frcn_eval.arguments[1]: dims_input
})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels,
dims)
nms_keep_indices = apply_nms_to_single_image_results(
regressed_rois,
labels,
scores,
nms_threshold=nms_threshold,
conf_threshold=nms_conf_threshold)
save_rois_to_file(regressed_rois,
nms_keep_indices,
labels,
str_labels,
scores,
results_base_path,
img_path,
headers=headers,
output_width_height=output_width_height,
suppressed_labels=suppressed_labels,
dims=dims)
timediff = datetime.now() - start
print(" time:", timediff)
def faster_rcnn_eval_and_plot(eval_model,
num_images_to_plot,
test_map_file,
class_map_file,
img_shape,
results_base_path,
feature_node_name,
classes,
draw_unregressed_rois=False,
draw_negative_rois=False,
nms_threshold=0.5,
nms_conf_threshold=0.0,
bgr_plot_threshold=0.8,
headers=True,
output_width_height=False,
suppressed_labels=()):
"""
Tests a Faster R-CNN model and plots images with detected boxes.
:param eval_model: the model to evaluate
:param num_images_to_plot: the number of images to plot
:param test_map_file: the map file with the test images/labels
:param class_map_file: the class map file
:param img_shape: the shape
:param results_base_path: the base of the output directory
:param feature_node_name: the feature node name
:param classes:
:param draw_unregressed_rois: whether to draw unregressed ROIs
:param draw_negative_rois: whether to draw negative ROIs (eg background)
:param nms_threshold:
:param nms_conf_threshold:
:param bgr_plot_threshold:
:param headers: whether to output the headers in the ROI file
:param output_width_height: whether to output x/y/width/height or x0/y0/x1/y1
"""
# load labels
str_labels = load_class_labels(class_map_file)
print("Class labels: %s" % str(str_labels))
# get image paths
with open(test_map_file) as f:
content = f.readlines()
img_base_path = os.path.dirname(os.path.abspath(test_map_file))
img_file_names = [
os.path.join(img_base_path,
x.split('\t')[1]) for x in content
]
# prepare model
image_input = input_variable(img_shape,
dynamic_axes=[Axis.default_batch_axis()],
name=feature_node_name)
dims_input = input_variable((1, 6),
dynamic_axes=[Axis.default_batch_axis()],
name='dims_input')
frcn_eval = eval_model(image_input, dims_input)
# dims_input_const = cntk.constant([image_width, image_height, image_width, image_height, image_width, image_height], (1, 6))
print("Plotting results from Faster R-CNN model for %s images." %
num_images_to_plot)
for i in range(0, num_images_to_plot):
img_path = img_file_names[i]
# save padded image
write_padded_image(
img_path, "{}/{}-padded.jpg".format(
results_base_path,
os.path.splitext(os.path.basename(img_path))[0]))
# evaluate single image
_, cntk_img_input, dims = load_resize_and_pad(img_path, img_shape[2],
img_shape[1])
dims_input = np.array(dims, dtype=np.float32)
dims_input.shape = (1, ) + dims_input.shape
output = frcn_eval.eval({
frcn_eval.arguments[0]: [cntk_img_input],
frcn_eval.arguments[1]: dims_input
})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1).tolist()
if draw_unregressed_rois:
# plot results without final regression
img_debug = faster_rcnn_visualize_results(
img_path,
labels,
scores,
out_rpn_rois,
img_shape[2],
img_shape[1],
classes,
nms_keep_indices=None,
draw_negative_rois=draw_negative_rois,
decision_threshold=bgr_plot_threshold)
imsave(
"{}/{}".format(results_base_path, os.path.basename(img_path)),
img_debug)
# apply regression and nms to bbox coordinates
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels,
dims)
nms_keep_indices = apply_nms_to_single_image_results(
regressed_rois,
labels,
scores,
nms_threshold=nms_threshold,
conf_threshold=nms_conf_threshold)
save_rois_to_file(regressed_rois,
nms_keep_indices,
labels,
str_labels,
scores,
results_base_path,
img_path,
headers=headers,
output_width_height=output_width_height,
suppressed_labels=suppressed_labels,
dims=dims)
img = faster_rcnn_visualize_results(
img_path,
labels,
scores,
regressed_rois,
img_shape[2],
img_shape[1],
classes,
nms_keep_indices=nms_keep_indices,
draw_negative_rois=draw_negative_rois,
decision_threshold=bgr_plot_threshold)
imsave(
"{}/{}-regr.jpg".format(
results_base_path,
os.path.splitext(os.path.basename(img_path)))[0], img)
