from detector import ObjectDetector
import numpy as np
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-a",
"--annotations",
required=True,
help="path to saved annotations...")
ap.add_argument("-i",
"--images",
required=True,
help="path to saved image paths...")
ap.add_argument("-d",
"--detector",
default=None,
help="path to save the trained detector...")
args = vars(ap.parse_args())
print("[INFO] loading annotations and images")
annots = np.load(args["annotations"])
imagePaths = np.load(args["images"])
detector = ObjectDetector()
print("[INFO] creating & saving object detector")
detector.fit(imagePaths, annots, visualize=False, savePath=args["detector"])
parse.add_argument("-i",
"--images",
help="Path to saved images",
default="train_images.npy")
parse.add_argument("-d",
"--detector",
help="Path to save model",
default="svm_model.svm")
parse.add_argument("-ta",
"--test_annotate",
help="Path to saved test boxes annotations",
default="test_annot.npy")
parse.add_argument("-tim",
"--test_images",
help="Path to test images",
default="test_images.npy")
args = vars(parse.parse_args())
annots = np.load(args["annotations"])
imagePaths = np.load(args["images"])
trainAnnot = np.load(args["test_annotate"])
trainImages = np.load(args["test_images"])
detector = ObjectDetector()
detector.fit(imagePaths,
annots,
trainAnnot,
trainImages,
visualize=True,
savePath=args["detector"])
from detector import ObjectDetector
import numpy as np
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-a", "--annotations", required=True, default='annot.npy', help="path to saved annotations...")
ap.add_argument("-i", "--images", required=True, default='images.npy', help="path to saved image paths...")
ap.add_argument("-d", "--detector", default='detector.svm', help="path to save the trained detector...")
args = vars(ap.parse_args())
print "[INFO] loading annotations and images"
annotations = np.load(args["annotations"])
image_paths = np.load(args["images"])
detector = ObjectDetector()
print "[INFO] creating & saving object detector"
detector.fit(image_paths, annotations, visualize=True, save_path=args["detector"])