import cv2 import numpy as np from car_detector.detector import car_detector, bow_features from car_detector.non_maximum import non_max_suppression_fast as nms from car_detector.pyramid import pyramid from car_detector.sliding_window import sliding_window def in_range(number, test, thresh=0.2): return abs(number - test) < thresh test_image = "../images/cars.jpg" svm, extractor = car_detector() detect = cv2.xfeatures2d.SIFT_create() w, h = 100, 40 img = cv2.imread(test_image) rectangles = [] counter = 1 scaleFactor = 1.25 scale = 1 font = cv2.FONT_HERSHEY_PLAIN for resized in pyramid(img, scaleFactor): scale = float(img.shape[1]) / float(resized.shape[1]) for (x, y, roi) in sliding_window(resized, 20, (100, 40)): if roi.shape[1] != w or roi.shape[0] != h:
import cv2 import numpy as np from car_detector.detector import car_detector, bow_features from car_detector.pyramid import pyramid from car_detector.non_maximum import non_max_suppression_fast as nms from car_detector.sliding_window import sliding_window import urllib def in_range(number, test, thresh=0.2): return abs(number - test) < thresh test_image = "/home/dvaliu/Downloads/CarData/TestImages/test-10.pgm" img_path = "/home/dvaliu/Downloads/CarData/TestImages/test-10.pgm" svm, extractor = car_detector() detect = cv2.xfeatures2d.SIFT_create() w, h = 100, 40 img = cv2.imread(img_path) #img = cv2.imread(test_image) rectangles = [] counter = 1 scaleFactor = 1.25 scale = 1 font = cv2.FONT_HERSHEY_PLAIN for resized in pyramid(img, scaleFactor): scale = float(img.shape[1]) / float(resized.shape[1]) for (x, y, roi) in sliding_window(resized, 20, (100, 40)): if roi.shape[1] != w or roi.shape[0] != h: