def evaluate_single_image(model, img_path, cfg):
"""
Computes detection results for the given model on the provided image
:param model: the model
:param img_path: the path to the image
:param cfg: the configuration
:return:
regressed_rois - the predicted bounding boxes
cls_probs - class probabilities per bounding box
"""
detector_name = _get_detector_name(cfg)
regressed_rois = None
cls_probs = None
print("detecting objects in image {}".format(img_path))
if detector_name == 'FastRCNN':
from FastRCNN.FastRCNN_eval import FastRCNN_Evaluator
evaluator = FastRCNN_Evaluator(model, cfg)
regressed_rois, cls_probs = evaluator.process_image(img_path)
elif detector_name == 'FasterRCNN':
from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
evaluator = FasterRCNN_Evaluator(model, cfg)
regressed_rois, cls_probs = evaluator.process_image(img_path)
else:
print('Unknown detector: {}'.format(detector_name))
return regressed_rois, cls_probs
def evaluate_single_image(model, img_path, cfg):
"""
Computes detection results for the given model on the provided image
:param model: the model
:param img_path: the path to the image
:param cfg: the configuration
:return:
regressed_rois - the predicted bounding boxes
cls_probs - class probabilities per bounding box
"""
detector_name = _get_detector_name(cfg)
regressed_rois = None
cls_probs = None
print("detecting objects in image {}".format(img_path))
if detector_name == 'FastRCNN':
from FastRCNN.FastRCNN_eval import FastRCNN_Evaluator
evaluator = FastRCNN_Evaluator(model, cfg)
regressed_rois, cls_probs = evaluator.process_image(img_path)
elif detector_name == 'FasterRCNN':
from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
evaluator = FasterRCNN_Evaluator(model, cfg)
regressed_rois, cls_probs = evaluator.process_image(img_path)
else:
print('Unknown detector: {}'.format(detector_name))
return regressed_rois, cls_probs
def measure_inference_time(model, img_path, cfg, num_repetitions=100):
"""
Computes detection results for the given model on the provided image
:param model: the model
:param img_path: the path to the image
:param cfg: the configuration
:return:
regressed_rois - the predicted bounding boxes
cls_probs - class probabilities per bounding box
"""
detector_name = _get_detector_name(cfg)
print(
"Measuring inference time (seconds per image) as average over {} runs".
format(num_repetitions))
if detector_name == 'FastRCNN':
from FastRCNN.FastRCNN_eval import FastRCNN_Evaluator
evaluator = FastRCNN_Evaluator(model, cfg)
elif detector_name == 'FasterRCNN':
from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
evaluator = FasterRCNN_Evaluator(model, cfg)
else:
print('Unknown detector: {}'.format(detector_name))
return
from time import time
start = time()
for i in range(num_repetitions):
_, _ = evaluator.process_image(img_path)
total = time() - start
print("seconds per image: {:2f} (total for {} images: {:2f})".format(
total / num_repetitions, num_repetitions, total))
def measure_inference_time(model, img_path, cfg, num_repetitions=100):
"""
Computes detection results for the given model on the provided image
:param model: the model
:param img_path: the path to the image
:param cfg: the configuration
:return:
regressed_rois - the predicted bounding boxes
cls_probs - class probabilities per bounding box
"""
detector_name = _get_detector_name(cfg)
print("Measuring inference time (seconds per image) as average over {} runs".format(num_repetitions))
if detector_name == 'FastRCNN':
from FastRCNN.FastRCNN_eval import FastRCNN_Evaluator
evaluator = FastRCNN_Evaluator(model, cfg)
elif detector_name == 'FasterRCNN':
from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
evaluator = FasterRCNN_Evaluator(model, cfg)
else:
print('Unknown detector: {}'.format(detector_name))
return
from time import time
start = time()
for i in range(num_repetitions):
_,_ = evaluator.process_image(img_path)
total = time() - start
print("seconds per image: {:2f} (total for {} images: {:2f})".format(total/num_repetitions, num_repetitions, total))