def load_and_run_detector(options, detector=None):
imageFileNames = options_to_images(options)
if options.forceCpu:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
print('Running detector on {} images'.format(len(imageFileNames)))
if len(imageFileNames) == 0:
print('Warning: no files available')
return
# Load detector if necessary
if detector is None:
startTime = time.time()
print('Loading model...')
detector = TFDetector(options.detectorFile)
elapsed = time.time() - startTime
print("Loaded model in {}".format(
humanfriendly.format_timespan(elapsed)))
# Run detector on target images
boxes, scores, classes, imageFileNames = generate_detections(
detector, imageFileNames, options)
assert len(boxes) == len(imageFileNames)
print('Writing output...')
df = detector_output_to_api_output(imageFileNames, options, boxes, scores,
classes)
# PERF: iterrows is the wrong way to do this for large files
if options.outputPathReplacements is not None:
for iRow, row in df.iterrows():
for query in options.outputPathReplacements:
replacement = options.outputPathReplacements[query]
row['image_path'] = row['image_path'].replace(
query, replacement)
if options.outputRelativeFilenames and os.path.isdir(options.imageFile):
for iRow, row in df.iterrows():
row['image_path'] = os.path.relpath(row['image_path'],
options.imageFile)
# While we're in transition between formats, write out the old format and
# convert to the new format
if options.outputFile.endswith('.csv'):
write_api_results(df, options.outputFile)
else:
tempfilename = next(tempfile._get_candidate_names()) + '.csv'
write_api_results(df, tempfilename)
convert_output_format.convert_csv_to_json(tempfilename,
options.outputFile)
os.remove(tempfilename)
return boxes, scores, classes, imageFileNames
def update_detection_table(RepeatDetectionResults,
options,
outputFilename=None):
detectionResults = RepeatDetectionResults.detectionResults
# An array of length nDirs, where each element is a list of DetectionLocation
# objects for that directory that have been flagged as suspicious
suspiciousDetectionsByDirectory = RepeatDetectionResults.suspiciousDetections
nBboxChanges = 0
print('Updating output table')
# For each suspicious detection (two loops)
for iDir, directoryEvents in enumerate(suspiciousDetectionsByDirectory):
for iDetectionEvent, detectionEvent in enumerate(directoryEvents):
locationBbox = detectionEvent.bbox
for iInstance, instance in enumerate(detectionEvent.instances):
instanceBbox = instance.bbox
# This should match the bbox for the detection event
iou = ct_utils.get_iou(instanceBbox, locationBbox)
# There are instances where iou is very close to the threshold so cannot use >
assert iou >= options.iouThreshold
assert instance.filename in RepeatDetectionResults.filenameToRow
iRow = RepeatDetectionResults.filenameToRow[instance.filename]
row = detectionResults.iloc[iRow]
rowDetections = row['detections']
detectionToModify = rowDetections[instance.iDetection]
# Make sure the bounding box matches
assert (instanceBbox[0:3] == detectionToModify['bbox'][0:3])
# Make the probability negative, if it hasn't been switched by
# another bounding box
if detectionToModify['conf'] >= 0:
detectionToModify['conf'] = -1 * detectionToModify['conf']
nBboxChanges += 1
# ...for each instance
# ...for each detection
# ...for each directory
# Update maximum probabilities
# For each row...
nProbChanges = 0
nProbChangesToNegative = 0
nProbChangesAcrossThreshold = 0
for iRow, row in detectionResults.iterrows():
detections = row['detections']
if len(detections) == 0:
continue
maxPOriginal = float(row['max_detection_conf'])
assert maxPOriginal >= 0
maxP = None
nNegative = 0
for iDetection, detection in enumerate(detections):
p = detection['conf']
if p < 0:
nNegative += 1
if (maxP is None) or (p > maxP):
maxP = p
if abs(maxP - maxPOriginal) > 0.00000001:
# We should only be making detections *less* likely
assert maxP < maxPOriginal
# row['max_confidence'] = str(maxP)
detectionResults.at[iRow, 'max_detection_conf'] = maxP
nProbChanges += 1
if maxP < 0:
nProbChangesToNegative += 1
if maxPOriginal >= options.confidenceMin and maxP < options.confidenceMin:
nProbChangesAcrossThreshold += 1
# Negative probabilities should be the only reason maxP changed, so
# we should have found at least one negative value
assert nNegative > 0
# ...if there was a change to the max probability for this row
# ...for each row
if outputFilename is not None:
write_api_results(detectionResults, RepeatDetectionResults.otherFields,
outputFilename)
print(
'Finished updating detection table\nChanged {} detections that impacted {} maxPs ({} to negative) ({} across confidence threshold)'
.format(nBboxChanges, nProbChanges, nProbChangesToNegative,
nProbChangesAcrossThreshold))
return detectionResults
def update_detection_table(RepeatDetectionResults,
options,
outputFilename=None):
detectionResults = RepeatDetectionResults.detectionResults
# An array of length nDirs, where each element is a list of DetectionLocation
# objects for that directory that have been flagged as suspicious
suspiciousDetectionsByDirectory = RepeatDetectionResults.suspiciousDetections
nBboxChanges = 0
print('Updating output table')
# For each directory
for iDir, directoryEvents in enumerate(suspiciousDetectionsByDirectory):
# For each suspicious detection group in this directory
for iDetectionEvent, detectionEvent in enumerate(directoryEvents):
locationBbox = detectionEvent.bbox
# For each instance of this suspicious detection
for iInstance, instance in enumerate(detectionEvent.instances):
instanceBbox = instance.bbox
# This should match the bbox for the detection event
iou = ct_utils.get_iou(instanceBbox, locationBbox)
# The bbox for this instance should be almost the same as the bbox
# for this detection group, where "almost" is defined by the IOU
# threshold.
assert iou >= options.iouThreshold
# if iou < options.iouThreshold:
# print('IOU warning: {},{}'.format(iou,options.iouThreshold))
assert instance.filename in RepeatDetectionResults.filenameToRow
iRow = RepeatDetectionResults.filenameToRow[instance.filename]
row = detectionResults.iloc[iRow]
rowDetections = row['detections']
detectionToModify = rowDetections[instance.iDetection]
# Make sure the bounding box matches
assert (instanceBbox[0:3] == detectionToModify['bbox'][0:3])
# Make the probability negative, if it hasn't been switched by
# another bounding box
if detectionToModify['conf'] >= 0:
detectionToModify['conf'] = -1 * detectionToModify['conf']
nBboxChanges += 1
# ...for each instance
# ...for each detection
# ...for each directory
# Update maximum probabilities
# For each row...
nProbChanges = 0
nProbChangesToNegative = 0
nProbChangesAcrossThreshold = 0
for iRow, row in detectionResults.iterrows():
detections = row['detections']
if isinstance(detections, float):
assert isinstance(row['failure'], str)
continue
if len(detections) == 0:
continue
maxPOriginal = float(row['max_detection_conf'])
# No longer strictly true; sometimes I run RDE on RDE output
# assert maxPOriginal >= 0
assert maxPOriginal >= -1.0
maxP = None
nNegative = 0
for iDetection, detection in enumerate(detections):
p = detection['conf']
if p < 0:
nNegative += 1
if (maxP is None) or (p > maxP):
maxP = p
if abs(maxP - maxPOriginal) > 1e-3:
# We should only be making detections *less* likely
assert maxP < maxPOriginal
# row['max_confidence'] = str(maxP)
detectionResults.at[iRow, 'max_detection_conf'] = maxP
nProbChanges += 1
if (maxP < 0) and (maxPOriginal >= 0):
nProbChangesToNegative += 1
if (maxPOriginal >=
options.confidenceMin) and (maxP < options.confidenceMin):
nProbChangesAcrossThreshold += 1
# Negative probabilities should be the only reason maxP changed, so
# we should have found at least one negative value
assert nNegative > 0
# ...if there was a meaningful change to the max probability for this row
# ...for each row
# If we're also writing output...
if outputFilename is not None and len(outputFilename) > 0:
write_api_results(detectionResults, RepeatDetectionResults.otherFields,
outputFilename)
print(
'Finished updating detection table\nChanged {} detections that impacted {} maxPs ({} to negative) ({} across confidence threshold)'
.format(nBboxChanges, nProbChanges, nProbChangesToNegative,
nProbChangesAcrossThreshold))
return detectionResults
