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"])