def test_detect(self, trainsetIdxs, testsetIdx, harvest=False, autoAnnotate=False, displayOptions=None):
"""
Detect objects in a video
:param harvest: whether to use harvesting
:param autoAnnotate: whether to automate harvesting (for testing purposes)
:param displayOptions: some option about what to display in the video
:return:
"""
self.log.info("Performing detection test")
framesStep = 10 # take every frameSteps-th frame
if not displayOptions:
displayOptions = {
'enabled': True, # display anything at all
'frame': True, # video frame image
'detections': False, # detected objects
'suppressed_detections': True,
'sliding_window': False,
'ground_truth': False
}
samples = self.getTrainSamples(trainsetIdxs)
clsf = self.trainFromSamples(samples) # train a classifier based on the given dataset samples
datasetId = self.datasetIds[testsetIdx] # get the dataset id given the dataset index
videoFileName = os.path.join(self.projectDirectory, 'videos', '%s.MOV' % datasetId)
annotationsFileName = os.path.join(self.projectDirectory, 'labels', "%s_output.txt" % datasetId)
video = Video(videoFileName, annotationsFileName)
video.cutoutSize = self.cutoutSize
detectionsTruth = video.getAnnotations() # get the labeled ground truth for this video
self.log.debug("Using video %s" % videoFileName)
self.suppressor = Suppressor()
cap = cv2.VideoCapture(videoFileName) # load the video file
results = [] # results for each frame
frameIdx = 0
while cap.isOpened():
ret, frame = cap.read()
if ret is False:
break
if ((frameIdx+1) % framesStep) == 0: # use every <frameStep>th frame
self.log.debug("Frame %d" % frameIdx)
truth = []
for truthDetection in detectionsTruth[frameIdx]:
if truthDetection['lost'] is True: # if the detection is not visible in this frame
continue
truthDetectionCutoutDimensions = video.getCutoutDimensions(truthDetection, frame.shape[:2]) # dimension of the truth in the frame
truth.append(truthDetectionCutoutDimensions)
detections = self.detectObjects(frame, clsf, truth, display=displayOptions) # detect objects in frame
result = self.matchDetections(detections, truth, frame.shape[:2]) # match the detections agains the truth
results.append(result)
if harvest:
detectionsOverlapRatios = result['detectionsOverlapRatios'] # how much the truth overlaps with each detection
newSamples = self.annotateDetections(frame, detections, detectionsOverlapRatios, autoAnnotate=autoAnnotate, overlapThreshold=0.3) # (manually) annotate all the found detections
if len(newSamples):
samples.extend(newSamples)
clsf = self.trainFromSamples(samples) # retrain a classifier based on the given dataset samples and new samples
frameIdx += 1
cap.release()
totalScore = sum([result['score'] for result in results])
totalPrecision = sum([result['precision'] for result in results])
totalRecall = sum([result['recall'] for result in results])
print ("= Average score:")
pprint(totalScore/len(results))
print ("= Average precision:")
pprint(totalPrecision/len(results))
print ("= Average recall:")
pprint(totalRecall/len(results))
__author__ = 'Rik Smit'
parser = argparse.ArgumentParser(description='Creating cutous from video using Vatic annotations.')
parser.add_argument('videofile', help='Location of the video file')
parser.add_argument('annotationsfile', help='Location of the annotations file')
parser.add_argument('outputdir', help='Location of the directory to store the cutouts')
parser.add_argument('--cutoutsize', help='Dimensions of the cutouts.', default=(100, 100))
parser.add_argument('--framestep', help='Use every <framestep>th frame.', default=25)
parser.add_argument('--negativescount', help='Amount of negatives to cutout for each frame.', default=50)
parser.add_argument('--pos', action='store_false', help="Whether to exclude extracting positives")
parser.add_argument('--neg', action='store_false', help="Whether to exclude extracting negatives")
args = parser.parse_args()
videoFileName = args.videofile
cutoutsDirectory = args.outputdir
annotationsFileName = args.annotationsfile
video = Video(videoFileName, annotationsFileName)
video.load()
video.cutoutSize = args.cutoutsize
video.negExamplesPerFrame = args.negativescount
video.framesStep = args.framestep
video.extractObjects(cutoutsDirectory, extractPositives=args.pos, extractNegatives=args.neg)
