from im_utils import *
import sys
import os
if not len(sys.argv) == 2:
print('Usage: {} <Video File>'.format(sys.argv[0]))
exit(1)
video = sys.argv[1]
if not os.path.exists(video):
print('Error! File {} does not exists'.format(video))
exit(1)
print('Loading Object Detector...')
detector = ObjectDetector('ssd/saved_model')
detector.loadModel()
#detector.getBoundingBoxes(cv2.imread('amur_small/002019.jpg'))
#detector.getBoundingBoxes(cv2.imread('amur_small/002019.jpg'))
print('Loading Identifiers...')
tigers_identifier = SvmIdentifier('amur-alexnet-relu6-linear-pca.model',
detector)
jaguars_identifier = SvmIdentifier('jaguar-alexnet-fc7-linear-pca.model',
detector)
elephants_identifier = SvmIdentifier('elp-alexnet-fc7-linear-pca.model',
detector)
tigers_identifier.loadModel()
jaguars_identifier.loadModel()
elephants_identifier.loadModel()
minId, maxId = min([x[1] for x in id_map]), max([x[1] for x in id_map])
idNum = None
if len(args) > 4:
idNum = int(args[4])
rev_map = {}
for x in id_map:
if not x[1] in rev_map:
rev_map[x[1]] = []
rev_map[x[1]].append(x[0])
print('Loading Object Detector')
det = ObjectDetector(objdetmodel)
det.SCORE_THRESHOLD = 0.7
det.loadModel()
# Warming up
det.getBoundingBoxes(readImageFromFile(os.path.join(folder,
rev_map[0][0])))
det.getBoundingBoxes(readImageFromFile(os.path.join(folder,
rev_map[0][0])))
print('Loading Object Identifier')
idn = SvmIdentifier(modelPath, det)
idn.loadModel()
if not idNum is None and not idn.isPresent(idNum):
print(
'Provided Id {} is not present in trained model, will choose random Id'
)
idNum = None
folder = 'amur/detection_train/trainval'
images = ['{}/{}'.format(folder, x) for x in os.listdir(folder)]
imagelist = np.random.choice(images, n, replace=False)
print('Measuring performance over {} images...'.format(len(images)))
config = args[1]
weights = args[2]
#model = YoloObjectDetector(config, weights, '../darknet/darknet/custom_data/detector.data')
#model.loadModel()
model = ObjectDetector(config)
model.loadModel()
print('Warming up...')
#model.getBoundingBoxes(images[0])
#model.getBoundingBoxes(images[1])
model.getBoundingBoxes(cv2.imread(images[0]))
model.getBoundingBoxes(cv2.imread(images[1]))
[cv2.imread(x) for x in imagelist]
print('Starting...')
times = []
for im in imagelist:
#model.getBoundingBoxes(im)
img = cv2.imread(im)
st = time.time()
model.getBoundingBoxes(img)
