def test_02_trainingse_nont(self): images = glob.glob('train_imgs/non-vehicles/*/*.png') (clf, scaler) = class_train.load() counter = 0 for i in images: img = cnst.img_read_f(i) test_img = cv2.resize(img, (64, 64)) features = single_img_features(test_img, color_space=cnst.color_space, spatial_size=cnst.spatial_size, hist_bins=cnst.hist_bins, orient=cnst.orient, pix_per_cell=cnst.pix_per_cell, cell_per_block=cnst.cell_per_block, hog_channel=cnst.hog_channel, spatial_feat=cnst.spatial_feat, hist_feat=cnst.hist_feat, hog_feat=cnst.hog_feat) test_features = scaler.transform(np.array(features).reshape(1, -1)) #test_features = features prediction = clf.predict(test_features) if (prediction > 0.0): print("Prediction " + str(prediction) + " for " + i) counter = counter + 1 print((len(images) - counter) / len(images))
def xtest_02_load(self): r = class_train.load() self.assertEqual(2, len(r)) print(type(r[0])) print(type(r[1])) self.assertIs(type(r[0]), sklearn.svm.classes.LinearSVC) self.assertIs(type(r[1]), sklearn.preprocessing.data.StandardScaler)
def test_01_multi(self): (svc, X_scaler) = class_train.load() image = cnst.img_read_f('test_images/test6.jpg') draw_image = np.copy(image) (hw, di) = hot_windows.multi_hot_wins(image, draw_image, svc, X_scaler, cnst.sizes) print(hw) mpimg.imsave("unit_test/test6_multi.jpg", di)
def test_01_ev(self): (svc, X_scaler) = class_train.load() image = cnst.img_read_f('test_images/test6.jpg') for s in sizes: draw_image = np.copy(image) (hw, di) = hot_windows.hot_wins(image, draw_image, svc, X_scaler, xy_window=(s, s)) print(hw) mpimg.imsave("unit_test/test6_" + str(s) + ".jpg", di)
def test_01_trainingset(self): images = glob.glob('train_imgs/vehicles/GTI_Far/image0000.png') (clf, scaler) = class_train.load() for i in images: img = cnst.img_read_f(i) test_img = cv2.resize(img, (64, 64)) features = single_img_features(test_img, color_space=cnst.color_space, spatial_size=cnst.spatial_size, hist_bins=cnst.hist_bins, orient=cnst.orient, pix_per_cell=cnst.pix_per_cell, cell_per_block=cnst.cell_per_block, hog_channel=cnst.hog_channel, spatial_feat=cnst.spatial_feat, hist_feat=cnst.hist_feat, hog_feat=cnst.hog_feat) print(features.shape) print(features) print(len(features)) test_features = scaler.transform(np.array(features).reshape(1, -1)) #test_features = features prediction = clf.predict(test_features) print(str(prediction) + " : " + i)
import numpy as np import cv2 import class_train import class_load import hot_windows import cv2 import matplotlib.image as mpimg import numpy as np import sys import cnst IMG_DIR = "project_video.mp4" TEST_OUT = "unit_test" (svc, X_scaler) = class_train.load() class TestVideo(unittest.TestCase): def test_pipe(self): uut.process(IMG_DIR, TEST_OUT + "/L" + IMG_DIR, cb_ok) #,subC=(12,18)) def cb(img): return img def cb_ok(image): draw_image = np.copy(image) # image = image.astype(np.float32)/255