Samples Python:

* Unused variables
* Bad identation

samples/python/asift.py

@@ -155,7 +155,7 @@ if __name__ == '__main__':
             H, status = None, None
             print('%d matches found, not enough for homography estimation' % len(p1))
 
-        vis = explore_match(win, img1, img2, kp_pairs, None, H)
+        explore_match(win, img1, img2, kp_pairs, None, H)
 
 
     match_and_draw('affine find_obj')

samples/python/browse.py

@@ -53,8 +53,8 @@ if __name__ == '__main__':
         small = cv2.pyrDown(small)
 
     def onmouse(event, x, y, flags, param):
-        h, w = img.shape[:2]
+        h, _w = img.shape[:2]
-        h1, w1 = small.shape[:2]
+        h1, _w1 = small.shape[:2]
         x, y = 1.0*x*h/h1, 1.0*y*h/h1
         zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5))
         cv2.imshow('zoom', zoom)

samples/python/camshift.py

@@ -41,7 +41,7 @@ from video import presets
 class App(object):
     def __init__(self, video_src):
         self.cam = video.create_capture(video_src, presets['cube'])
-        ret, self.frame = self.cam.read()
+        _ret, self.frame = self.cam.read()
         cv2.namedWindow('camshift')
         cv2.setMouseCallback('camshift', self.onmouse)
 
@@ -76,7 +76,7 @@ class App(object):
 
     def run(self):
         while True:
-            ret, self.frame = self.cam.read()
+            _ret, self.frame = self.cam.read()
             vis = self.frame.copy()
             hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
             mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

samples/python/demo.py

@@ -99,7 +99,7 @@ class App:
         run_btn = tk.Button(right, command=self.on_run, text='Run', width=8)
 
         self.text = text = ScrolledText(right, font=('arial', 12, 'normal'), width = 30, wrap='word')
-        self.linker = linker = LinkManager(text, self.on_link)
+        self.linker = _linker = LinkManager(text, self.on_link)
         self.text.tag_config("header1", font=('arial', 14, 'bold'))
         self.text.tag_config("header2", font=('arial', 12, 'bold'))
         text.config(state='disabled')

samples/python/digits.py

@@ -84,7 +84,7 @@ class KNearest(StatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.findNearest(samples, self.k)
+        _retval, results, _neigh_resp, _dists = self.model.findNearest(samples, self.k)
         return results.ravel()
 
 class SVM(StatModel):

samples/python/digits_video.py

@@ -35,7 +35,7 @@ def main():
         model.load_(classifier_fn) #Known bug: https://github.com/opencv/opencv/issues/4969
 
     while True:
-        ret, frame = cap.read()
+        _ret, frame = cap.read()
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
 
@@ -59,12 +59,12 @@ def main():
             cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
 
             bin_roi = bin[y:,x:][:h,:w]
-            gray_roi = gray[y:,x:][:h,:w]
 
             m = bin_roi != 0
             if not 0.1 < m.mean() < 0.4:
                 continue
             '''
+            gray_roi = gray[y:,x:][:h,:w]
             v_in, v_out = gray_roi[m], gray_roi[~m]
             if v_out.std() > 10.0:
                 continue

samples/python/find_obj.py

@@ -92,7 +92,6 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
 
     green = (0, 255, 0)
     red = (0, 0, 255)
-    white = (255, 255, 255)
     kp_color = (51, 103, 236)
     for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
         if inlier:
@@ -183,7 +182,7 @@ if __name__ == '__main__':
             H, status = None, None
             print('%d matches found, not enough for homography estimation' % len(p1))
 
-        vis = explore_match(win, img1, img2, kp_pairs, status, H)
+        _vis = explore_match(win, img1, img2, kp_pairs, status, H)
 
     match_and_draw('find_obj')
     cv2.waitKey()

samples/python/gaussian_mix.py

@@ -15,7 +15,7 @@ import cv2
 def make_gaussians(cluster_n, img_size):
     points = []
     ref_distrs = []
-    for i in xrange(cluster_n):
+    for _i in xrange(cluster_n):
         mean = (0.1 + 0.8*random.rand(2)) * img_size
         a = (random.rand(2, 2)-0.5)*img_size*0.1
         cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
@@ -28,7 +28,7 @@ def make_gaussians(cluster_n, img_size):
 
 def draw_gaussain(img, mean, cov, color):
     x, y = np.int32(mean)
-    w, u, vt = cv2.SVDecomp(cov)
+    w, u, _vt = cv2.SVDecomp(cov)
     ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
     s1, s2 = np.sqrt(w)*3.0
     cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.LINE_AA)

samples/python/lappyr.py

@@ -28,7 +28,7 @@ from common import nothing, getsize
 def build_lappyr(img, leveln=6, dtype=np.int16):
     img = dtype(img)
     levels = []
-    for i in xrange(leveln-1):
+    for _i in xrange(leveln-1):
         next_img = cv2.pyrDown(img)
         img1 = cv2.pyrUp(next_img, dstsize=getsize(img))
         levels.append(img-img1)

samples/python/letter_recog.py

@@ -64,12 +64,11 @@ class RTrees(LetterStatModel):
         self.model = cv2.ml.RTrees_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
         self.model.setMaxDepth(20)
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.ravel()
 
 
@@ -81,7 +80,7 @@ class KNearest(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.findNearest(samples, k = 10)
+        _retval, results, _neigh_resp, _dists = self.model.findNearest(samples, k = 10)
         return results.ravel()
 
 
@@ -90,7 +89,7 @@ class Boost(LetterStatModel):
         self.model = cv2.ml.Boost_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
+        _sample_n, var_n = samples.shape
         new_samples = self.unroll_samples(samples)
         new_responses = self.unroll_responses(responses)
         var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL, cv2.ml.VAR_CATEGORICAL], np.uint8)
@@ -101,7 +100,7 @@ class Boost(LetterStatModel):
 
     def predict(self, samples):
         new_samples = self.unroll_samples(samples)
-        ret, resp = self.model.predict(new_samples)
+        _ret, resp = self.model.predict(new_samples)
 
         return resp.ravel().reshape(-1, self.class_n).argmax(1)
 
@@ -118,7 +117,7 @@ class SVM(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.ravel()
 
 
@@ -127,7 +126,7 @@ class MLP(LetterStatModel):
         self.model = cv2.ml.ANN_MLP_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
+        _sample_n, var_n = samples.shape
         new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
         layer_sizes = np.int32([var_n, 100, 100, self.class_n])
 
@@ -141,7 +140,7 @@ class MLP(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, np.float32(new_responses))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.argmax(-1)
 
 

samples/python/lk_homography.py

@@ -39,8 +39,8 @@ feature_params = dict( maxCorners = 1000,
                        blockSize = 19 )
 
 def checkedTrace(img0, img1, p0, back_threshold = 1.0):
-    p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
+    p1, _st, _err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
-    p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
+    p0r, _st, _err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
     d = abs(p0-p0r).reshape(-1, 2).max(-1)
     status = d < back_threshold
     return p1, status
@@ -56,7 +56,7 @@ class App:
 
     def run(self):
         while True:
-            ret, frame = self.cam.read()
+            _ret, frame = self.cam.read()
             frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
             vis = frame.copy()
             if self.p0 is not None:

samples/python/lk_track.py

@@ -46,15 +46,15 @@ class App:
 
     def run(self):
         while True:
-            ret, frame = self.cam.read()
+            _ret, frame = self.cam.read()
             frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
             vis = frame.copy()
 
             if len(self.tracks) > 0:
                 img0, img1 = self.prev_gray, frame_gray
                 p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
-                p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
+                p1, _st, _err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
-                p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
+                p0r, _st, _err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                 d = abs(p0-p0r).reshape(-1, 2).max(-1)
                 good = d < 1
                 new_tracks = []

samples/python/mosse.py

@@ -73,7 +73,7 @@ class MOSSE:
         self.G = cv2.dft(g, flags=cv2.DFT_COMPLEX_OUTPUT)
         self.H1 = np.zeros_like(self.G)
         self.H2 = np.zeros_like(self.G)
-        for i in xrange(128):
+        for _i in xrange(128):
             a = self.preprocess(rnd_warp(img))
             A = cv2.dft(a, flags=cv2.DFT_COMPLEX_OUTPUT)
             self.H1 += cv2.mulSpectrums(self.G, A, 0, conjB=True)

samples/python/mouse_and_match.py

@@ -38,7 +38,7 @@ def onmouse(event, x, y, flags, param):
             patch = gray[sel[1]:sel[3],sel[0]:sel[2]]
             result = cv2.matchTemplate(gray,patch,cv2.TM_CCOEFF_NORMED)
             result = np.abs(result)**3
-            val, result = cv2.threshold(result, 0.01, 0, cv2.THRESH_TOZERO)
+            _val, result = cv2.threshold(result, 0.01, 0, cv2.THRESH_TOZERO)
             result8 = cv2.normalize(result,None,0,255,cv2.NORM_MINMAX,cv2.CV_8U)
             cv2.imshow("result", result8)
         drag_start = None

samples/python/opt_flow.py

@@ -29,7 +29,7 @@ def draw_flow(img, flow, step=16):
     lines = np.int32(lines + 0.5)
     vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
     cv2.polylines(vis, lines, 0, (0, 255, 0))
-    for (x1, y1), (x2, y2) in lines:
+    for (x1, y1), (_x2, _y2) in lines:
         cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
     return vis
 

samples/python/plane_ar.py

@@ -92,7 +92,7 @@ class App:
                         [0, fx*w, 0.5*(h-1)],
                         [0.0,0.0,      1.0]])
         dist_coef = np.zeros(4)
-        ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef)
+        _ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef)
         verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0)
         verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
         for i, j in ar_edges:

samples/python/squares.py

@@ -30,8 +30,8 @@ def find_squares(img):
                 bin = cv2.Canny(gray, 0, 50, apertureSize=5)
                 bin = cv2.dilate(bin, None)
             else:
-                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
+                _retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
-            bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+            bin, contours, _hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
             for cnt in contours:
                 cnt_len = cv2.arcLength(cnt, True)
                 cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)

samples/python/turing.py

@@ -45,7 +45,7 @@ if __name__ == '__main__':
 
     def process_scale(a_lods, lod):
         d = a_lods[lod] - cv2.pyrUp(a_lods[lod+1])
-        for i in xrange(lod):
+        for _i in xrange(lod):
             d = cv2.pyrUp(d)
         v = cv2.GaussianBlur(d*d, (3, 3), 0)
         return np.sign(d), v