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Merge pull request #15718 from alalek:pylint_warnings

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This commit is contained in:
Alexander Alekhin 2019-10-17 10:46:02 +00:00
commit 17e9fde75a
26 changed files with 40 additions and 39 deletions
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2
modules/python/test/test_misc.py
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@ -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

2
samples/dnn/fast_neural_style.py
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@ -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)

2
samples/dnn/mobilenet_ssd_accuracy.py
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@ -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):

2
samples/dnn/text_detection.py
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@ -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))

2
samples/dnn/tf_text_graph_common.py
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@ -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:

2
samples/dnn/tf_text_graph_ssd.py
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@ -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)

2
samples/python/browse.py
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@ -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):

4
samples/python/calibrate.py
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@ -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')

2
samples/python/camera_calibration_show_extrinsics.py
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@ -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')

2
samples/python/color_histogram.py
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@ -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)

2
samples/python/edge.py
View File

@ -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')

2
samples/python/facedetect.py
View File

@ -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)

1
samples/python/fitline.py
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@ -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:

2
samples/python/houghcircles.py
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@ -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

4
samples/python/houghlines.py
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@ -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]

2
samples/python/kmeans.py
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@ -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()):

2
samples/python/lappyr.py
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@ -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):

4
samples/python/opt_flow.py
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@ -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

2
samples/python/peopledetect.py
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@ -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):

4
samples/python/stereo_match.py
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@ -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)

2
samples/python/turing.py
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@ -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:

18
samples/python/tutorial_code/core/mat_operations/mat_operations.py
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@ -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():

4
samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py
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@ -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

4
samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py
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@ -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]

2
samples/python/video_threaded.py
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@ -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

2
samples/python/video_v4l2.py
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@ -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))