From 48073e6d95daffba3a327a33d3752c8f255c9a6b Mon Sep 17 00:00:00 2001 From: Alexander Alekhin Date: Fri, 1 Nov 2019 18:59:35 +0300 Subject: [PATCH] pylint: eliminate warnings --- modules/python/test/test_cuda.py | 44 ++++++++++++++-------------- samples/python/dis_opt_flow.py | 6 ++-- samples/python/stitching.py | 2 +- samples/python/stitching_detailed.py | 30 +++++++++---------- 4 files changed, 41 insertions(+), 41 deletions(-) diff --git a/modules/python/test/test_cuda.py b/modules/python/test/test_cuda.py index ea81664509..2484cb2bde 100644 --- a/modules/python/test/test_cuda.py +++ b/modules/python/test/test_cuda.py @@ -95,8 +95,8 @@ class cuda_test(NewOpenCVTests): def test_cudabgsegm_existence(self): #Test at least the existence of wrapped functions for now - bgsub = cv.cuda.createBackgroundSubtractorMOG() - bgsub = cv.cuda.createBackgroundSubtractorMOG2() + _bgsub = cv.cuda.createBackgroundSubtractorMOG() + _bgsub = cv.cuda.createBackgroundSubtractorMOG2() self.assertTrue(True) #It is sufficient that no exceptions have been there @@ -104,8 +104,8 @@ class cuda_test(NewOpenCVTests): #Test at least the existence of wrapped functions for now try: - writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30) - reader = cv.cudacodec.createVideoReader("tmp") + _writer = cv.cudacodec.createVideoWriter("tmp", (128, 128), 30) + _reader = cv.cudacodec.createVideoReader("tmp") except cv.error as e: self.assertEqual(e.code, cv.Error.StsNotImplemented) self.skipTest("NVCUVENC is not installed") @@ -125,11 +125,11 @@ class cuda_test(NewOpenCVTests): cuMat2 = cv.cuda.cvtColor(cuMat2, cv.COLOR_RGB2GRAY) fast = cv.cuda_FastFeatureDetector.create() - kps = fast.detectAsync(cuMat1) + _kps = fast.detectAsync(cuMat1) orb = cv.cuda_ORB.create() - kps1, descs1 = orb.detectAndComputeAsync(cuMat1, None) - kps2, descs2 = orb.detectAndComputeAsync(cuMat2, None) + _kps1, descs1 = orb.detectAndComputeAsync(cuMat1, None) + _kps2, descs2 = orb.detectAndComputeAsync(cuMat2, None) bf = cv.cuda_DescriptorMatcher.createBFMatcher(cv.NORM_HAMMING) matches = bf.match(descs1, descs2) @@ -144,20 +144,20 @@ class cuda_test(NewOpenCVTests): def test_cudafilters_existence(self): #Test at least the existence of wrapped functions for now - filter = cv.cuda.createBoxFilter(cv.CV_8UC1, -1, (3, 3)) - filter = cv.cuda.createLinearFilter(cv.CV_8UC4, -1, np.eye(3)) - filter = cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3) - filter = cv.cuda.createSeparableLinearFilter(cv.CV_8UC1, -1, np.eye(3), np.eye(3)) - filter = cv.cuda.createDerivFilter(cv.CV_8UC1, -1, 1, 1, 3) - filter = cv.cuda.createSobelFilter(cv.CV_8UC1, -1, 1, 1) - filter = cv.cuda.createScharrFilter(cv.CV_8UC1, -1, 1, 0) - filter = cv.cuda.createGaussianFilter(cv.CV_8UC1, -1, (3, 3), 16) - filter = cv.cuda.createMorphologyFilter(cv.MORPH_DILATE, cv.CV_32FC1, np.eye(3)) - filter = cv.cuda.createBoxMaxFilter(cv.CV_8UC1, (3, 3)) - filter = cv.cuda.createBoxMinFilter(cv.CV_8UC1, (3, 3)) - filter = cv.cuda.createRowSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) - filter = cv.cuda.createColumnSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) - filter = cv.cuda.createMedianFilter(cv.CV_8UC1, 3) + _filter = cv.cuda.createBoxFilter(cv.CV_8UC1, -1, (3, 3)) + _filter = cv.cuda.createLinearFilter(cv.CV_8UC4, -1, np.eye(3)) + _filter = cv.cuda.createLaplacianFilter(cv.CV_16UC1, -1, ksize=3) + _filter = cv.cuda.createSeparableLinearFilter(cv.CV_8UC1, -1, np.eye(3), np.eye(3)) + _filter = cv.cuda.createDerivFilter(cv.CV_8UC1, -1, 1, 1, 3) + _filter = cv.cuda.createSobelFilter(cv.CV_8UC1, -1, 1, 1) + _filter = cv.cuda.createScharrFilter(cv.CV_8UC1, -1, 1, 0) + _filter = cv.cuda.createGaussianFilter(cv.CV_8UC1, -1, (3, 3), 16) + _filter = cv.cuda.createMorphologyFilter(cv.MORPH_DILATE, cv.CV_32FC1, np.eye(3)) + _filter = cv.cuda.createBoxMaxFilter(cv.CV_8UC1, (3, 3)) + _filter = cv.cuda.createBoxMinFilter(cv.CV_8UC1, (3, 3)) + _filter = cv.cuda.createRowSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) + _filter = cv.cuda.createColumnSumFilter(cv.CV_8UC1, cv.CV_32FC1, 3) + _filter = cv.cuda.createMedianFilter(cv.CV_8UC1, 3) self.assertTrue(True) #It is sufficient that no exceptions have been there @@ -195,7 +195,7 @@ class cuda_test(NewOpenCVTests): cv.cuda.meanShiftSegmentation(cuC4, 10, 5, 5).download() clahe = cv.cuda.createCLAHE() - clahe.apply(cuC1, cv.cuda_Stream.Null()); + clahe.apply(cuC1, cv.cuda_Stream.Null()) histLevels = cv.cuda.histEven(cuC3, 20, 0, 255) cv.cuda.histRange(cuC1, histLevels) diff --git a/samples/python/dis_opt_flow.py b/samples/python/dis_opt_flow.py index 845ad48fbe..14127aef34 100755 --- a/samples/python/dis_opt_flow.py +++ b/samples/python/dis_opt_flow.py @@ -30,7 +30,7 @@ def draw_flow(img, flow, step=16): lines = np.int32(lines + 0.5) vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR) cv.polylines(vis, lines, 0, (0, 255, 0)) - for (x1, y1), (x2, y2) in lines: + for (x1, y1), (_x2, _y2) in lines: cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1) return vis @@ -66,7 +66,7 @@ def main(): fn = 0 cam = video.create_capture(fn) - ret, prev = cam.read() + _ret, prev = cam.read() prevgray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY) show_hsv = False show_glitch = False @@ -78,7 +78,7 @@ def main(): flow = None while True: - ret, img = cam.read() + _ret, img = cam.read() gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) if flow is not None and use_temporal_propagation: #warp previous flow to get an initial approximation for the current flow: diff --git a/samples/python/stitching.py b/samples/python/stitching.py index 97d94e3ebd..18a160ec5b 100644 --- a/samples/python/stitching.py +++ b/samples/python/stitching.py @@ -50,7 +50,7 @@ def main(): print("Can't stitch images, error code = %d" % status) sys.exit(-1) - cv.imwrite(args.output, pano); + cv.imwrite(args.output, pano) print("stitching completed successfully. %s saved!" % args.output) print('Done') diff --git a/samples/python/stitching_detailed.py b/samples/python/stitching_detailed.py index 8f08b86978..a1b05e27fb 100644 --- a/samples/python/stitching_detailed.py +++ b/samples/python/stitching_detailed.py @@ -48,7 +48,7 @@ def main(): args = parser.parse_args() img_names=args.img_names print(img_names) - preview = args.preview + _preview = args.preview try_cuda = args.try_cuda work_megapix = args.work_megapix seam_megapix = args.seam_megapix @@ -84,7 +84,7 @@ def main(): print("Bad exposure compensation method") exit() expos_comp_nr_feeds = args.expos_comp_nr_feeds - expos_comp_nr_filtering = args.expos_comp_nr_filtering + _expos_comp_nr_filtering = args.expos_comp_nr_filtering expos_comp_block_size = args.expos_comp_block_size match_conf = args.match_conf seam_find_type = args.seam @@ -118,7 +118,7 @@ def main(): images=[] is_work_scale_set = False is_seam_scale_set = False - is_compose_scale_set = False; + is_compose_scale_set = False for name in img_names: full_img = cv.imread(cv.samples.findFile(name)) if full_img is None: @@ -163,9 +163,9 @@ def main(): img_names_subset.append(img_names[indices[i,0]]) img_subset.append(images[indices[i,0]]) full_img_sizes_subset.append(full_img_sizes[indices[i,0]]) - images = img_subset; - img_names = img_names_subset; - full_img_sizes = full_img_sizes_subset; + images = img_subset + img_names = img_names_subset + full_img_sizes = full_img_sizes_subset num_images = len(img_names) if num_images < 2: print("Need more images") @@ -266,7 +266,7 @@ def main(): if seam_find_type == "no": seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) elif seam_find_type == "voronoi": - seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM); + seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) elif seam_find_type == "gc_color": seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR") elif seam_find_type == "gc_colorgrad": @@ -279,7 +279,7 @@ def main(): print("Can't create the following seam finder ",seam_find_type) exit() seam_finder.find(images_warped_f, corners,masks_warped ) - imgListe=[] + _imgListe=[] compose_scale=1 corners=[] sizes=[] @@ -294,8 +294,8 @@ def main(): if not is_compose_scale_set: if compose_megapix > 0: compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1]))) - is_compose_scale_set = True; - compose_work_aspect = compose_scale / work_scale; + is_compose_scale_set = True + compose_work_aspect = compose_scale / work_scale warped_image_scale *= compose_work_aspect warper = cv.PyRotationWarper(warp_type,warped_image_scale) for i in range(0,len(img_names)): @@ -304,14 +304,14 @@ def main(): cameras[i].ppy *= compose_work_aspect sz = (full_img_sizes[i][0] * compose_scale,full_img_sizes[i][1]* compose_scale) K = cameras[i].K().astype(np.float32) - roi = warper.warpRoi(sz, K, cameras[i].R); + roi = warper.warpRoi(sz, K, cameras[i].R) corners.append(roi[0:2]) sizes.append(roi[2:4]) if abs(compose_scale - 1) > 1e-1: img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT) else: - img = full_img; - img_size = (img.shape[1],img.shape[0]); + img = full_img + _img_size = (img.shape[1],img.shape[0]) K=cameras[idx].K().astype(np.float32) corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT) mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8) @@ -341,9 +341,9 @@ def main(): if timelapse: matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8) timelapser.process(image_warped_s, matones, corners[idx]) - pos_s = img_names[idx].rfind("/"); + pos_s = img_names[idx].rfind("/") if pos_s == -1: - fixedFileName = "fixed_" + img_names[idx]; + fixedFileName = "fixed_" + img_names[idx] else: fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ] cv.imwrite(fixedFileName, timelapser.getDst())