class apiModel():
def __init__(self, model_path, model_name):
self.model_name = model_name
nms_thresh = 0.3
conf_thresh = 0.5
cfg_path = glob(os.path.join(model_path, '*cfg'))[0]
weight_path = glob(os.path.join(model_path, '*weights'))[0]
self.det = ObjectDetector(cfg_path, weight_path, conf_thresh,
nms_thresh, 0)
name_path = glob(os.path.join(model_path, '*names'))[0]
self.class_names = open(name_path).read().splitlines()
def _predict_api(self, url):
image = url_to_image(url)
if not np.any(image):
return []
image = self.det.format_image(image)
detections, _, _ = self.det.detect_object(*image)
results = []
for bbox in detections:
class_name = self.class_names[bbox.cls]
results.append({
"model":
self.model_name,
"url":
url,
"label":
class_name,
"score":
round(float(bbox.confidence), 2),
"bbox": [
float(bbox.top),
float(bbox.left),
float(bbox.bottom),
float(bbox.right)
]
})
return results
def predict_api(self, urls):
urls = urls or []
urls = filter(None, urls)
if len(urls) == 0:
return []
results = [self._predict_api(url) for url in urls]
return [obj for image in results for obj in image]
def __init__(self, model_path, model_name):
self.model_name = model_name
nms_thresh = 0.3
conf_thresh = 0.5
cfg_path = glob(os.path.join(model_path, '*cfg'))[0]
weight_path = glob(os.path.join(model_path, '*weights'))[0]
self.det = ObjectDetector(cfg_path, weight_path, conf_thresh,
nms_thresh, 0)
name_path = glob(os.path.join(model_path, '*names'))[0]
self.class_names = open(name_path).read().splitlines()
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
]
det = ObjectDetector("../cfg/yolo.cfg", "../yolo.weights")
url = "http://farm9.staticflickr.com/8323/8141398311_2fd0af60f7.jpg"
# for i in xrange(4):
rst, run_time = det.detect_object(_get_image(url))
print "got {} objects in {} seconds".format(len(rst), run_time)
for bbox in rst:
print "{} {} {} {} {} {}".format(
voc_names[bbox.cls], bbox.top, bbox.left, bbox.bottom, bbox.right, bbox.confidence
)
from detector import Darknet_ObjectDetector as ObjectDetector
from detector import DetBBox
if __name__ == '__main__':
from PIL import Image
det = ObjectDetector('../cfg/yolo.cfg','/media/ssd/models/yolo/yolo.weights')
for i in xrange(4):
rst, run_time = det.detect_object(Image.open('../data/dog.jpg'))
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(bbox.top, bbox.left, bbox.bottom, bbox.right, bbox.cls, bbox.confidence)
from detector import Darknet_ObjectDetector as ObjectDetector
from detector import DetBBox
import requests
from PIL import Image
from PIL import ImageFilter
from StringIO import StringIO
def _get_image(url):
return Image.open(StringIO(requests.get(url).content))
if __name__ == '__main__':
from PIL import Image
voc_names = ["aeroplane", "bicycle", "bird", "boat", "bottle","bus", "car", "cat", "chair", "cow", "diningtable","dog", "horse", "motorbike", "person", "pottedplant","sheep", "sofa", "train", "tvmonitor"]
det = ObjectDetector('../cfg/yolo.cfg','../yolo.weights')
url = 'http://farm9.staticflickr.com/8323/8141398311_2fd0af60f7.jpg'
#for i in xrange(4):
rst, run_time = det.detect_object(_get_image(url))
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(voc_names[bbox.cls], bbox.top, bbox.left, bbox.bottom, bbox.right, bbox.confidence)
from detector import Darknet_ObjectDetector as ObjectDetector
from detector import DetBBox
import requests
from PIL import Image
from PIL import ImageFilter
from StringIO import StringIO
import cv2
def _get_image(url):
return Image.open(StringIO(requests.get(url).content))
if __name__ == '__main__':
ObjectDetector.set_device(0)
from PIL import Image
names = open('../data/coco.names').read().splitlines()
det = ObjectDetector('../cfg/yolo.cfg', '../yolo.weights')
img = Image.open("../data/dog.jpg")
rst, run_time = det.detect_object(img)
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(names[bbox.cls], bbox.top, bbox.left,
bbox.bottom, bbox.right,
bbox.confidence)
parser = argparse.ArgumentParser()
parser.add_argument('--name-path', type=str, default=defaults['name_path'])
parser.add_argument('--cfg-path', type=str, default=defaults['cfg_path'])
parser.add_argument('--weight-path',
type=str,
default=defaults['weight_path'])
parser.add_argument('--thresh', type=float, default=0.5)
parser.add_argument('--nms', type=float, default=0.4)
parser.add_argument('--draw', action='store_true')
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
ObjectDetector.set_device(0)
det = ObjectDetector(args.cfg_path, args.weight_path, args.thresh,
args.nms, int(args.draw))
class_names = open(args.name_path).read().splitlines()
print class_names
start = time()
c_load_time, c_pred_time = 0, 0
for i, im_name in enumerate(sys.stdin):
py_all_time = time() - start
py_load_time = py_all_time - c_load_time - c_pred_time
print >> sys.stderr, "%d | pyall %f | pyload %f | cload %f | cpred %f" % (
i, py_all_time, py_load_time, c_load_time, c_pred_time)
try:
import requests
from PIL import Image
from PIL import ImageFilter
from StringIO import StringIO
import cv2
def _get_image(url):
return Image.open(StringIO(requests.get(url).content))
if __name__ == '__main__':
from PIL import Image
voc_names = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
det = ObjectDetector('../cfg/yolo.cfg', '../yolo.weights')
#url = 'http://farm9.staticflickr.com/8323/8141398311_2fd0af60f7.jpg'
#for i in xrange(4):
img = Image.open("deng.jpg")
rst, run_time = det.detect_object(img)
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(voc_names[bbox.cls], bbox.top,
bbox.left, bbox.bottom, bbox.right,
bbox.confidence)
from PIL import Image
from PIL import ImageFilter
from StringIO import StringIO
def _get_image(path):
#return Image.open(StringIO(requests.get(url).content))
return Image.open(path)
if __name__ == '__main__':
from PIL import Image
voc_names = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor", "plate_license"
]
det = ObjectDetector('../cfg/yolo-plate.cfg',
'/home/himon/code/yolo/yolo-plate_final.weights')
#url = 'http://farm9.staticflickr.com/8323/8141398311_2fd0af60f7.jpg'
path = '/home/himon/code/yolo/cars.jpg'
#for i in xrange(4):
rst, run_time = det.detect_object(_get_image(path))
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(voc_names[bbox.cls], bbox.top,
bbox.left, bbox.bottom, bbox.right,
bbox.confidence)
from detector import Darknet_ObjectDetector as ObjectDetector
from detector import DetBBox
import requests
from PIL import Image
from PIL import ImageFilter
from StringIO import StringIO
import cv2
def _get_image(url):
return Image.open(StringIO(requests.get(url).content))
if __name__ == '__main__':
from PIL import Image
voc_names = ["aeroplane", "bicycle", "bird", "boat", "bottle","bus", "car", "cat", "chair", "cow", "diningtable","dog", "horse", "motorbike", "person", "pottedplant","sheep", "sofa", "train", "tvmonitor"]
det = ObjectDetector('../cfg/yolo.cfg','../yolo.weights')
#url = 'http://farm9.staticflickr.com/8323/8141398311_2fd0af60f7.jpg'
#for i in xrange(4):
img = Image.open("deng.jpg")
rst, run_time = det.detect_object(img)
print 'got {} objects in {} seconds'.format(len(rst), run_time)
for bbox in rst:
print '{} {} {} {} {} {}'.format(voc_names[bbox.cls], bbox.top, bbox.left, bbox.bottom, bbox.right, bbox.confidence)
