Merge pull request #15718 from alalek:pylint_warnings

modules/python/test/test_misc.py

@@ -96,7 +96,7 @@ class SamplesFindFile(NewOpenCVTests):
 
     def test_MissingFileException(self):
         try:
-            res = cv.samples.findFile('non_existed.file', True)
+            _res = cv.samples.findFile('non_existed.file', True)
             self.assertEqual("Dead code", 0)
         except cv.error as _e:
             pass

samples/dnn/fast_neural_style.py

@@ -14,7 +14,7 @@ parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of
 args = parser.parse_args()
 
 net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
-net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
 
 if args.input:
     cap = cv.VideoCapture(args.input)

samples/dnn/mobilenet_ssd_accuracy.py

@@ -27,7 +27,7 @@ args = parser.parse_args()
 
 ### Get OpenCV predictions #####################################################
 net = cv.dnn.readNetFromTensorflow(cv.samples.findFile(args.weights), cv.samples.findFile(args.prototxt))
-net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
 
 detections = []
 for imgName in os.listdir(args.images):

samples/dnn/text_detection.py

@@ -134,7 +134,7 @@ def main():
             for j in range(4):
                 p1 = (vertices[j][0], vertices[j][1])
                 p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])
-                cv.line(frame, p1, p2, (0, 255, 0), 1);
+                cv.line(frame, p1, p2, (0, 255, 0), 1)
 
         # Put efficiency information
         cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))

samples/dnn/tf_text_graph_common.py

@@ -21,7 +21,7 @@ def tokenize(s):
                 elif token:
                     tokens.append(token)
                     token = ""
-            isString = (symbol == '\"' or symbol == '\'') ^ isString;
+            isString = (symbol == '\"' or symbol == '\'') ^ isString
 
         elif symbol == '{' or symbol == '}' or symbol == '[' or symbol == ']':
             if token:

samples/dnn/tf_text_graph_ssd.py

@@ -122,7 +122,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
     print('Input image size: %dx%d' % (image_width, image_height))
 
     # Read the graph.
-    inpNames = ['image_tensor']
+    _inpNames = ['image_tensor']
     outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
 
     writeTextGraph(modelPath, outputPath, outNames)

samples/python/browse.py

@@ -45,7 +45,7 @@ def main():
 
 
     small = img
-    for i in xrange(3):
+    for _i in xrange(3):
         small = cv.pyrDown(small)
 
     def onmouse(event, x, y, flags, param):

samples/python/calibrate.py

@@ -97,7 +97,7 @@ def main():
         obj_points.append(pattern_points)
 
     # calculate camera distortion
-    rms, camera_matrix, dist_coefs, rvecs, tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None)
+    rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None)
 
     print("\nRMS:", rms)
     print("camera matrix:\n", camera_matrix)
@@ -106,7 +106,7 @@ def main():
     # undistort the image with the calibration
     print('')
     for fn in img_names if debug_dir else []:
-        path, name, ext = splitfn(fn)
+        _path, name, _ext = splitfn(fn)
         img_found = os.path.join(debug_dir, name + '_chess.png')
         outfile = os.path.join(debug_dir, name + '_undistorted.png')
 

samples/python/camera_calibration_show_extrinsics.py

@@ -184,7 +184,7 @@ def main():
     extrinsics = fs.getNode('extrinsic_parameters').mat()
 
     import matplotlib.pyplot as plt
-    from mpl_toolkits.mplot3d import Axes3D
+    from mpl_toolkits.mplot3d import Axes3D  # pylint: disable=unused-variable
 
     fig = plt.figure()
     ax = fig.gca(projection='3d')

samples/python/color_histogram.py

@@ -46,7 +46,7 @@ class App():
         cam = video.create_capture(fn, fallback='synth:bg=baboon.jpg:class=chess:noise=0.05')
 
         while True:
-            flag, frame = cam.read()
+            _flag, frame = cam.read()
             cv.imshow('camera', frame)
 
             small = cv.pyrDown(frame)

samples/python/edge.py

@@ -38,7 +38,7 @@ def main():
 
     cap = video.create_capture(fn)
     while True:
-        flag, img = cap.read()
+        _flag, img = cap.read()
         gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
         thrs1 = cv.getTrackbarPos('thrs1', 'edge')
         thrs2 = cv.getTrackbarPos('thrs2', 'edge')

samples/python/facedetect.py

@@ -48,7 +48,7 @@ def main():
     cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('samples/data/lena.jpg')))
 
     while True:
-        ret, img = cam.read()
+        _ret, img = cam.read()
         gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
         gray = cv.equalizeHist(gray)
 

samples/python/fitline.py

@@ -88,6 +88,7 @@ def main():
         update()
         ch = cv.waitKey(0)
         if ch == ord('f'):
+            global cur_func_name
             if PY3:
                 cur_func_name = next(dist_func_names)
             else:

samples/python/houghcircles.py

@@ -30,7 +30,7 @@ def main():
     circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)
 
     if circles is not None: # Check if circles have been found and only then iterate over these and add them to the image
-        a, b, c = circles.shape
+        _a, b, _c = circles.shape
         for i in range(b):
             cv.circle(cimg, (circles[0][i][0], circles[0][i][1]), circles[0][i][2], (0, 0, 255), 3, cv.LINE_AA)
             cv.circle(cimg, (circles[0][i][0], circles[0][i][1]), 2, (0, 255, 0), 3, cv.LINE_AA)  # draw center of circle

samples/python/houghlines.py

@@ -29,14 +29,14 @@ def main():
 
     if True: # HoughLinesP
         lines = cv.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
-        a,b,c = lines.shape
+        a, b, _c = lines.shape
         for i in range(a):
             cv.line(cdst, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv.LINE_AA)
 
     else:    # HoughLines
         lines = cv.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
         if lines is not None:
-            a,b,c = lines.shape
+            a, b, _c = lines.shape
             for i in range(a):
                 rho = lines[i][0][0]
                 theta = lines[i][0][1]

samples/python/kmeans.py

@@ -33,7 +33,7 @@ def main():
         points, _ = make_gaussians(cluster_n, img_size)
 
         term_crit = (cv.TERM_CRITERIA_EPS, 30, 0.1)
-        ret, labels, centers = cv.kmeans(points, cluster_n, None, term_crit, 10, 0)
+        _ret, labels, _centers = cv.kmeans(points, cluster_n, None, term_crit, 10, 0)
 
         img = np.zeros((img_size, img_size, 3), np.uint8)
         for (x, y), label in zip(np.int32(points), labels.ravel()):

samples/python/lappyr.py

@@ -60,7 +60,7 @@ def main():
         cv.createTrackbar('%d'%i, 'level control', 5, 50, nothing)
 
     while True:
-        ret, frame = cap.read()
+        _ret, frame = cap.read()
 
         pyr = build_lappyr(frame, leveln)
         for i in xrange(leveln):

samples/python/opt_flow.py

@@ -64,14 +64,14 @@ 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
     cur_glitch = prev.copy()
 
     while True:
-        ret, img = cam.read()
+        _ret, img = cam.read()
         gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
         flow = cv.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
         prevgray = gray

samples/python/peopledetect.py

@@ -51,7 +51,7 @@ def main():
             print('loading error')
             continue
 
-        found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
+        found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
         found_filtered = []
         for ri, r in enumerate(found):
             for qi, q in enumerate(found):

samples/python/stereo_match.py

@@ -69,8 +69,8 @@ def main():
     out_points = points[mask]
     out_colors = colors[mask]
     out_fn = 'out.ply'
-    write_ply('out.ply', out_points, out_colors)
-    print('%s saved' % 'out.ply')
+    write_ply(out_fn, out_points, out_colors)
+    print('%s saved' % out_fn)
 
     cv.imshow('left', imgL)
     cv.imshow('disparity', (disp-min_disp)/num_disp)

samples/python/turing.py

@@ -32,7 +32,7 @@ def main():
 
     w, h = 512, 512
 
-    args, args_list = getopt.getopt(sys.argv[1:], 'o:', [])
+    args, _args_list = getopt.getopt(sys.argv[1:], 'o:', [])
     args = dict(args)
     out = None
     if '-o' in args:

samples/python/tutorial_code/core/mat_operations/mat_operations.py

@@ -25,13 +25,13 @@ def access_pixel():
     y = 0
     x = 0
     ## [Pixel access 1]
-    intensity = img[y,x]
+    _intensity = img[y,x]
     ## [Pixel access 1]
 
     ## [Pixel access 3]
-    blue = img[y,x,0]
-    green = img[y,x,1]
-    red = img[y,x,2]
+    _blue = img[y,x,0]
+    _green = img[y,x,1]
+    _red = img[y,x,2]
     ## [Pixel access 3]
 
     ## [Pixel access 5]
@@ -42,12 +42,12 @@ def reference_counting():
     # Memory management and reference counting
     ## [Reference counting 2]
     img = cv.imread('image.jpg')
-    img1 = np.copy(img)
+    _img1 = np.copy(img)
     ## [Reference counting 2]
 
     ## [Reference counting 3]
     img = cv.imread('image.jpg')
-    sobelx = cv.Sobel(img, cv.CV_32F, 1, 0);
+    _sobelx = cv.Sobel(img, cv.CV_32F, 1, 0)
     ## [Reference counting 3]
 
 def primitive_operations():
@@ -57,17 +57,17 @@ def primitive_operations():
     ## [Set image to black]
 
     ## [Select ROI]
-    smallImg = img[10:110,10:110]
+    _smallImg = img[10:110,10:110]
     ## [Select ROI]
 
     ## [BGR to Gray]
     img = cv.imread('image.jpg')
-    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    _grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
     ## [BGR to Gray]
 
     src = np.ones((4,4), np.uint8)
     ## [Convert to CV_32F]
-    dst = src.astype(np.float32)
+    _dst = src.astype(np.float32)
     ## [Convert to CV_32F]
 
 def visualize_images():

samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py

@@ -25,8 +25,8 @@ def gammaCorrection():
     res = cv.LUT(img_original, lookUpTable)
     ## [changing-contrast-brightness-gamma-correction]
 
-    img_gamma_corrected = cv.hconcat([img_original, res]);
-    cv.imshow("Gamma correction", img_gamma_corrected);
+    img_gamma_corrected = cv.hconcat([img_original, res])
+    cv.imshow("Gamma correction", img_gamma_corrected)
 
 def on_linear_transform_alpha_trackbar(val):
     global alpha

samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py

@@ -85,13 +85,13 @@ _, contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
 
 for i, c in enumerate(contours):
     # Calculate the area of each contour
-    area = cv.contourArea(c);
+    area = cv.contourArea(c)
     # Ignore contours that are too small or too large
     if area < 1e2 or 1e5 < area:
         continue
 
     # Draw each contour only for visualisation purposes
-    cv.drawContours(src, contours, i, (0, 0, 255), 2);
+    cv.drawContours(src, contours, i, (0, 0, 255), 2)
     # Find the orientation of each shape
     getOrientation(c, src)
 ## [contours]

samples/python/video_threaded.py

@@ -70,7 +70,7 @@ def main():
             draw_str(res, (20, 60), "frame interval :  %.1f ms" % (frame_interval.value*1000))
             cv.imshow('threaded video', res)
         if len(pending) < threadn:
-            ret, frame = cap.read()
+            _ret, frame = cap.read()
             t = clock()
             frame_interval.update(t - last_frame_time)
             last_frame_time = t

samples/python/video_v4l2.py

@@ -42,7 +42,7 @@ def main():
     cv.createTrackbar("Focus", "Video", focus, 100, lambda v: cap.set(cv.CAP_PROP_FOCUS, v / 100))
 
     while True:
-        status, img = cap.read()
+        _status, img = cap.read()
 
         fourcc = decode_fourcc(cap.get(cv.CAP_PROP_FOURCC))