|
| | test_harris_standalone.debug_level = logging.DEBUG |
| |
| | test_harris_standalone.stream |
| |
| | test_harris_standalone.stdout |
| |
| | test_harris_standalone.level |
| |
| int | test_harris_standalone.REPEATS = 5 |
| |
| list | test_harris_standalone.kernels |
| |
| | test_harris_standalone.frame = cv.imread(cv.samples.findFile("CARLA.jpg")) |
| |
| | test_harris_standalone.orig_frame = cv.cvtColor(frame, cv.COLOR_RGB2GRAY) |
| |
| | test_harris_standalone.toc_capture = time.perf_counter() |
| |
| list | test_harris_standalone.elapsed_times = [[0 for j in range(REPEATS)] for i in range(len(kernels))] |
| |
| list | test_harris_standalone.results = [[0 for j in range(REPEATS)] for i in range(len(kernels))] |
| |
| | test_harris_standalone.start_time = time.time() |
| |
| int | test_harris_standalone.blockSize = 2 |
| |
| int | test_harris_standalone.ksize = 3 |
| |
| float | test_harris_standalone.freeParameter = 0.04 |
| |
| | test_harris_standalone.tmp = frame |
| |
| list | test_harris_standalone.channels = [0] |
| |
| | test_harris_standalone.mask = None |
| |
| list | test_harris_standalone.histSize = [256] |
| |
| list | test_harris_standalone.ranges = [0,256] |
| |
| | test_harris_standalone.hist = cv.calcHist(tmp, channels, mask, histSize, ranges) |
| |
| int | test_harris_standalone.t_lower = 50 |
| |
| int | test_harris_standalone.t_upper = 150 |
| |
| | test_harris_standalone.canny = cv.Canny(frame, t_lower, t_upper) |
| |
| | test_harris_standalone.lines = cv.HoughLines(canny, 1, np.pi / 180, 150, None, 0, 0) |
| |
| | test_harris_standalone.x = cv.Sobel(frame, cv.CV_64F, 1, 0, ksize=5) |
| |
| | test_harris_standalone.y = cv.Sobel(frame, cv.CV_64F, 0, 1, ksize=5) |
| |
| | test_harris_standalone.absx = cv.convertScaleAbs(x) |
| |
| | test_harris_standalone.absy = cv.convertScaleAbs(y) |
| |
| | test_harris_standalone.ddepth = cv.CV_16S |
| |
| int | test_harris_standalone.kernel_size = 5 |
| |
| | test_harris_standalone.source |
| |
| | test_harris_standalone.destination |
| |
| | test_harris_standalone.grid_x |
| |
| | test_harris_standalone.grid_y |
| |
| | test_harris_standalone.grid_z = griddata(destination, source, (grid_x, grid_y), method='cubic') |
| |
| | test_harris_standalone.map_x = np.append([], [ar[:,1] for ar in grid_z]).reshape(784,784) |
| |
| | test_harris_standalone.map_y = np.append([], [ar[:,0] for ar in grid_z]).reshape(784,784) |
| |
| | test_harris_standalone.map_x_32 = map_x.astype('float32') |
| |
| | test_harris_standalone.map_y_32 = map_y.astype('float32') |
| |
| | test_harris_standalone.rows |
| |
| | test_harris_standalone.cols |
| |
| | test_harris_standalone.input_pts = np.float32([[0,0], [cols-1,0], [0,rows-1]]) |
| |
| | test_harris_standalone.output_pts = np.float32([[cols-1,0], [0,0], [cols-1,rows-1]]) |
| |
| | test_harris_standalone.M = cv.getAffineTransform(input_pts , output_pts) |
| |
| | test_harris_standalone.hog = cv.HOGDescriptor() |
| |
| | test_harris_standalone.h = hog.compute(frame) |
| |
| | test_harris_standalone.end_time = time.time() |
| |
| string | test_harris_standalone.image_name = "CARLA_out_kernel_" + kernels[i] + ".jpg" |
| |
| int | test_harris_standalone.average_elapsed_time = sum(elapsed_times) / REPEATS |
| |
| | test_harris_standalone.df = pd.DataFrame(elapsed_times) |
| |
| | test_harris_standalone.ax = sns.boxplot(data=df) |
| |
| | test_harris_standalone.loc |
| |
| | test_harris_standalone.axis |
| |
| | test_harris_standalone.labelrotation |
| |
| | test_harris_standalone.fontsize |
| |
| | test_harris_standalone.x1 |
| |
| | test_harris_standalone.y1 |
| |
| | test_harris_standalone.x2 |
| |
| | test_harris_standalone.y2 |
| |
1.9.1