# -*- coding: utf-8 -*-
"""
Interface to Faster R-CNN object proposals.
"""
import logging
import utool as ut
import vtool as vt
from os.path import abspath, dirname, expanduser, join, exists # NOQA
import numpy as np
import sys
import cv2
(print, rrr, profile) = ut.inject2(__name__, '[faster r-cnn]')
logger = logging.getLogger('wbia')
# SCRIPT_PATH = abspath(dirname(__file__))
SCRIPT_PATH = abspath(expanduser(join('~', 'code', 'py-faster-rcnn')))
if not ut.get_argflag('--no-faster-rcnn'):
try:
assert exists(SCRIPT_PATH)
def add_path(path):
# if path not in sys.path:
sys.path.insert(0, path)
# Add pycaffe to PYTHONPATH
pycaffe_path = join(SCRIPT_PATH, 'caffe-fast-rcnn', 'python')
add_path(pycaffe_path)
# Add caffe lib path to PYTHONPATH
lib_path = join(SCRIPT_PATH, 'lib')
add_path(lib_path)
import caffe
ut.reload_module(caffe)
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
# from fast_rcnn.nms_wrapper import nms
except AssertionError:
logger.info(
'WARNING Failed to find py-faster-rcnn. ' 'Faster R-CNN is unavailable'
)
# if ut.SUPER_STRICT:
# raise
except ImportError:
logger.info('WARNING Failed to import fast_rcnn. ' 'Faster R-CNN is unavailable')
# if ut.SUPER_STRICT:
# raise
VERBOSE_SS = ut.get_argflag('--verbdss') or ut.VERBOSE
CONFIG_URL_DICT = {
# 'pretrained-fast-vgg-pascal' : 'https://wildbookiarepository.azureedge.net/models/pretrained.fastrcnn.vgg16.pascal.prototxt', # Trained on PASCAL VOC 2007
'pretrained-vgg-pascal': 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.vgg16.pascal.prototxt', # Trained on PASCAL VOC 2007
'pretrained-zf-pascal': 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.zf.pascal.prototxt', # Trained on PASCAL VOC 2007
'pretrained-vgg-ilsvrc': 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.vgg16.ilsvrc.prototxt', # Trained on ILSVRC 2014
'pretrained-zf-ilsvrc': 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.zf.ilsvrc.prototxt', # Trained on ILSVRC 2014
'default': 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.vgg16.pascal.prototxt', # Trained on PASCAL VOC 2007
None: 'https://wildbookiarepository.azureedge.net/models/pretrained.fasterrcnn.vgg16.pascal.prototxt', # Trained on PASCAL VOC 2007
}
def _parse_weight_from_cfg(url):
return url.replace('.prototxt', '.caffemodel')
def _parse_classes_from_cfg(url):
return url.replace('.prototxt', '.classes')
def _parse_class_list(classes_filepath):
# Load classes from file into the class list
assert exists(classes_filepath)
class_list = []
with open(classes_filepath) as classes:
for line in classes.readlines():
line = line.strip()
if len(line) > 0:
class_list.append(line)
return class_list
[docs]def detect_gid_list(ibs, gid_list, downsample=True, verbose=VERBOSE_SS, **kwargs):
"""
Args:
gid_list (list of int): the list of IBEIS image_rowids that need detection
downsample (bool, optional): a flag to indicate if the original image
sizes should be used; defaults to True
True: ibs.get_image_detectpaths() is used
False: ibs.get_image_paths() is used
Kwargs (optional): refer to the Faster R-CNN documentation for configuration settings
Args:
ibs (wbia.IBEISController): image analysis api
gid_list (list of int): the list of IBEIS image_rowids that need detection
downsample (bool, optional): a flag to indicate if the original image
sizes should be used; defaults to True
Kwargs:
detector, config_filepath, weights_filepath, verbose
Yields:
tuple: (gid, gpath, result_list)
CommandLine:
python -m wbia.algo.detect.fasterrcnn detect_gid_list --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.detect.fasterrcnn import * # NOQA
>>> from wbia.core_images import LocalizerConfig
>>> import wbia
>>> ibs = wbia.opendb('testdb1')
>>> gid_list = ibs.get_valid_gids()
>>> config = {'verbose': True}
>>> downsample = False
>>> results_list = detect_gid_list(ibs, gid_list, downsample, **config)
>>> results_list = list(results_list)
>>> print('result lens = %r' % (map(len, list(results_list))))
>>> print('result[0] = %r' % (len(list(results_list[0][2]))))
>>> config = {'verbose': True}
>>> downsample = False
>>> results_list = detect_gid_list(ibs, gid_list, downsample, **config)
>>> results_list = list(results_list)
>>> print('result lens = %r' % (map(len, list(results_list))))
>>> print('result[0] = %r' % (len(list(results_list[0][2]))))
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> ut.show_if_requested()
Yields:
results (list of dict)
"""
# Get new gpaths if downsampling
if downsample:
gpath_list = ibs.get_image_detectpaths(gid_list)
neww_list = [vt.open_image_size(gpath)[0] for gpath in gpath_list]
oldw_list = [oldw for (oldw, oldh) in ibs.get_image_sizes(gid_list)]
downsample_list = [oldw / neww for oldw, neww in zip(oldw_list, neww_list)]
orient_list = [1] * len(gid_list)
else:
gpath_list = ibs.get_image_paths(gid_list)
downsample_list = [None] * len(gpath_list)
orient_list = ibs.get_image_orientation(gid_list)
# Run detection
results_iter = detect(gpath_list, verbose=verbose, **kwargs)
# Upscale the results
_iter = zip(downsample_list, gid_list, orient_list, results_iter)
for downsample, gid, orient, (gpath, result_list) in _iter:
# Upscale the results back up to the original image size
for result in result_list:
if downsample is not None and downsample != 1.0:
for key in ['xtl', 'ytl', 'width', 'height']:
result[key] = int(result[key] * downsample)
bbox = (
result['xtl'],
result['ytl'],
result['width'],
result['height'],
)
bbox_list = [bbox]
bbox = bbox_list[0]
result['xtl'], result['ytl'], result['width'], result['height'] = bbox
yield (gid, gpath, result_list)
[docs]def detect(
gpath_list,
config_filepath,
weight_filepath,
class_filepath,
sensitivity,
verbose=VERBOSE_SS,
use_gpu=True,
use_gpu_id=0,
**kwargs,
):
"""
Args:
gpath_list (list of str): the list of image paths that need proposal candidates
Kwargs (optional): refer to the Faster R-CNN documentation for configuration settings
Returns:
iter
"""
cfg.TEST.HAS_RPN = True # Use RPN for proposals
# Get correct config if specified with shorthand
config_url = None
if config_filepath in CONFIG_URL_DICT:
config_url = CONFIG_URL_DICT[config_filepath]
config_filepath = ut.grab_file_url(config_url, appname='wbia', check_hash=True)
# Get correct weights if specified with shorthand
if weight_filepath in CONFIG_URL_DICT:
if weight_filepath is None and config_url is not None:
config_url_ = config_url
else:
config_url_ = CONFIG_URL_DICT[weight_filepath]
weight_url = _parse_weight_from_cfg(config_url_)
weight_filepath = ut.grab_file_url(weight_url, appname='wbia', check_hash=True)
if class_filepath is None:
class_url = _parse_classes_from_cfg(config_url)
class_filepath = ut.grab_file_url(
class_url, appname='wbia', check_hash=True, verbose=verbose
)
class_list = _parse_class_list(class_filepath)
# Need to convert unicode strings to Python strings to support Boost Python
# call signatures in caffe
prototxt_filepath = str(config_filepath) # alias to Caffe nomenclature
caffemodel_filepath = str(weight_filepath) # alias to Caffe nomenclature
assert exists(prototxt_filepath), 'Specified prototxt file not found'
assert exists(caffemodel_filepath), 'Specified caffemodel file not found'
if use_gpu:
caffe.set_mode_gpu()
caffe.set_device(use_gpu_id)
cfg.GPU_ID = use_gpu_id
else:
caffe.set_mode_cpu()
net = caffe.Net(prototxt_filepath, caffemodel_filepath, caffe.TEST)
# Warm-up network on a dummy image
im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
for i in range(2):
_, _ = im_detect(net, im)
results_list_ = []
for gpath in gpath_list:
image = cv2.imread(gpath)
score_list, bbox_list = im_detect(net, image)
# Compile results
result_list_ = []
for class_index, class_name in enumerate(class_list[1:]):
class_index += 1 # because we skipped background
class_boxes = bbox_list[:, 4 * class_index : 4 * (class_index + 1)]
class_scores = score_list[:, class_index]
dets_list = np.hstack((class_boxes, class_scores[:, np.newaxis]))
dets_list = dets_list.astype(np.float32)
# # Perform NMS
# keep_list = nms(dets_list, nms_sensitivity)
# dets_list = dets_list[keep_list, :]
# Perform sensitivity check
keep_list = np.where(dets_list[:, -1] >= sensitivity)[0]
dets_list = dets_list[keep_list, :]
for (xtl, ytl, xbr, ybr, conf) in dets_list:
xtl = int(np.around(xtl))
ytl = int(np.around(ytl))
xbr = int(np.around(xbr))
ybr = int(np.around(ybr))
confidence = float(conf)
result_dict = {
'xtl': xtl,
'ytl': ytl,
'width': xbr - xtl,
'height': ybr - ytl,
'class': class_name,
'confidence': confidence,
}
result_list_.append(result_dict)
results_list_.append(result_list_)
results_list = zip(gpath_list, results_list_)
return results_list