Add segmentation and object tracking python tests

modules/features2d/include/opencv2/features2d.hpp

@@ -361,7 +361,7 @@ public:
     */
     CV_WRAP virtual void detectRegions( InputArray image,
                                         CV_OUT std::vector<std::vector<Point> >& msers,
-                                        std::vector<Rect>& bboxes ) = 0;
+                                        CV_OUT std::vector<Rect>& bboxes ) = 0;
 
     CV_WRAP virtual void setDelta(int delta) = 0;
     CV_WRAP virtual int getDelta() const = 0;

modules/python/test/test_camshift.py

@@ -39,7 +39,7 @@ class camshift_test(NewOpenCVTests):
 
     def prepareRender(self):
 
-        self.render = TestSceneRender(self.get_sample('samples/data/pca_test1.jpg'), True)
+        self.render = TestSceneRender(self.get_sample('samples/data/pca_test1.jpg'), deformation = True)
 
     def runTracker(self):
 
@@ -53,7 +53,6 @@ class camshift_test(NewOpenCVTests):
         while True:
             framesCounter += 1
             self.frame = self.render.getNextFrame()
-            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.)))
 
@@ -67,11 +66,6 @@ class camshift_test(NewOpenCVTests):
                 hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
                 cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
                 self.hist = hist.reshape(-1)
-
-                vis_roi = vis[y0:y1, x0:x1]
-                cv2.bitwise_not(vis_roi, vis_roi)
-                vis[mask == 0] = 0
-
                 self.selection = False
 
             if self.track_window and self.track_window[2] > 0 and self.track_window[3] > 0:
@@ -81,8 +75,6 @@ class camshift_test(NewOpenCVTests):
                 term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
                 track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
 
-                if self.show_backproj:
-                    vis[:] = prob[...,np.newaxis]
             trackingRect = np.array(self.track_window)
             trackingRect[2] += trackingRect[0]
             trackingRect[3] += trackingRect[1]

modules/python/test/test_feature_homography.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+
+'''
+Feature homography
+==================
+
+Example of using features2d framework for interactive video homography matching.
+ORB features and FLANN matcher are used. The actual tracking is implemented by
+PlaneTracker class in plane_tracker.py
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import sys
+PY3 = sys.version_info[0] == 3
+
+if PY3:
+    xrange = range
+
+# local modules
+from tst_scene_render import TestSceneRender
+
+def intersectionRate(s1, s2):
+
+    x1, y1, x2, y2 = s1
+    s1 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
+
+    area, intersection = cv2.intersectConvexConvex(s1, np.array(s2))
+    return 2 * area / (cv2.contourArea(s1) + cv2.contourArea(np.array(s2)))
+
+from tests_common import NewOpenCVTests
+
+class feature_homography_test(NewOpenCVTests):
+
+    render = None
+    tracker = None
+    framesCounter = 0
+    frame = None
+
+    def test_feature_homography(self):
+
+        self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'),
+            self.get_sample('samples/data/box.png'), noise = 0.5, speed = 0.5)
+        self.frame = self.render.getNextFrame()
+        self.tracker = PlaneTracker()
+        self.tracker.clear()
+        self.tracker.add_target(self.frame, self.render.getCurrentRect())
+
+        while self.framesCounter < 100:
+            self.framesCounter += 1
+            tracked = self.tracker.track(self.frame)
+            if len(tracked) > 0:
+                tracked = tracked[0]
+                self.assertGreater(intersectionRate(self.render.getCurrentRect(), np.int32(tracked.quad)), 0.6)
+            else:
+                self.assertEqual(0, 1, 'Tracking error')
+            self.frame = self.render.getNextFrame()
+
+
+# built-in modules
+from collections import namedtuple
+
+FLANN_INDEX_KDTREE = 1
+FLANN_INDEX_LSH    = 6
+flann_params= dict(algorithm = FLANN_INDEX_LSH,
+                   table_number = 6, # 12
+                   key_size = 12,     # 20
+                   multi_probe_level = 1) #2
+
+MIN_MATCH_COUNT = 10
+
+'''
+  image     - image to track
+  rect      - tracked rectangle (x1, y1, x2, y2)
+  keypoints - keypoints detected inside rect
+  descrs    - their descriptors
+  data      - some user-provided data
+'''
+PlanarTarget = namedtuple('PlaneTarget', 'image, rect, keypoints, descrs, data')
+
+'''
+  target - reference to PlanarTarget
+  p0     - matched points coords in target image
+  p1     - matched points coords in input frame
+  H      - homography matrix from p0 to p1
+  quad   - target bounary quad in input frame
+'''
+TrackedTarget = namedtuple('TrackedTarget', 'target, p0, p1, H, quad')
+
+class PlaneTracker:
+    def __init__(self):
+        self.detector = cv2.ORB_create( nfeatures = 1000 )
+        self.matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
+        self.targets = []
+        self.frame_points = []
+
+    def add_target(self, image, rect, data=None):
+        '''Add a new tracking target.'''
+        x0, y0, x1, y1 = rect
+        raw_points, raw_descrs = self.detect_features(image)
+        points, descs = [], []
+        for kp, desc in zip(raw_points, raw_descrs):
+            x, y = kp.pt
+            if x0 <= x <= x1 and y0 <= y <= y1:
+                points.append(kp)
+                descs.append(desc)
+        descs = np.uint8(descs)
+        self.matcher.add([descs])
+        target = PlanarTarget(image = image, rect=rect, keypoints = points, descrs=descs, data=data)
+        self.targets.append(target)
+
+    def clear(self):
+        '''Remove all targets'''
+        self.targets = []
+        self.matcher.clear()
+
+    def track(self, frame):
+        '''Returns a list of detected TrackedTarget objects'''
+        self.frame_points, frame_descrs = self.detect_features(frame)
+        if len(self.frame_points) < MIN_MATCH_COUNT:
+            return []
+        matches = self.matcher.knnMatch(frame_descrs, k = 2)
+        matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
+        if len(matches) < MIN_MATCH_COUNT:
+            return []
+        matches_by_id = [[] for _ in xrange(len(self.targets))]
+        for m in matches:
+            matches_by_id[m.imgIdx].append(m)
+        tracked = []
+        for imgIdx, matches in enumerate(matches_by_id):
+            if len(matches) < MIN_MATCH_COUNT:
+                continue
+            target = self.targets[imgIdx]
+            p0 = [target.keypoints[m.trainIdx].pt for m in matches]
+            p1 = [self.frame_points[m.queryIdx].pt for m in matches]
+            p0, p1 = np.float32((p0, p1))
+            H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
+            status = status.ravel() != 0
+            if status.sum() < MIN_MATCH_COUNT:
+                continue
+            p0, p1 = p0[status], p1[status]
+
+            x0, y0, x1, y1 = target.rect
+            quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
+            quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)
+
+            track = TrackedTarget(target=target, p0=p0, p1=p1, H=H, quad=quad)
+            tracked.append(track)
+        tracked.sort(key = lambda t: len(t.p0), reverse=True)
+        return tracked
+
+    def detect_features(self, frame):
+        '''detect_features(self, frame) -> keypoints, descrs'''
+        keypoints, descrs = self.detector.detectAndCompute(frame, None)
+        if descrs is None:  # detectAndCompute returns descs=None if not keypoints found
+            descrs = []
+        return keypoints, descrs

modules/python/test/test_grabcut.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+'''
+===============================================================================
+Interactive Image Segmentation using GrabCut algorithm.
+===============================================================================
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import sys
+
+from tests_common import NewOpenCVTests
+
+class grabcut_test(NewOpenCVTests):
+
+    def verify(self, mask, exp):
+
+        maxDiffRatio = 0.02
+        expArea = np.count_nonzero(exp)
+        nonIntersectArea = np.count_nonzero(mask != exp)
+        curRatio = float(nonIntersectArea) / expArea
+        return curRatio < maxDiffRatio
+
+    def scaleMask(self, mask):
+
+        return np.where((mask==cv2.GC_FGD) + (mask==cv2.GC_PR_FGD),255,0).astype('uint8')
+
+    def test_grabcut(self):
+
+        img = self.get_sample('cv/shared/airplane.png')
+        mask_prob = self.get_sample("cv/grabcut/mask_probpy.png", 0)
+        exp_mask1 = self.get_sample("cv/grabcut/exp_mask1py.png", 0)
+        exp_mask2 = self.get_sample("cv/grabcut/exp_mask2py.png", 0)
+
+        if img == None:
+            self.assertEqual(0, 1, 'Missing test data')
+
+        rect = (24, 126, 459, 168)
+        mask = np.zeros(img.shape[:2], dtype = np.uint8)
+        bgdModel = np.zeros((1,65),np.float64)
+        fgdModel = np.zeros((1,65),np.float64)
+        cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 0, cv2.GC_INIT_WITH_RECT)
+        cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 2, cv2.GC_EVAL)
+
+        if mask_prob == None:
+            mask_prob = mask.copy()
+            cv2.imwrite(self.extraTestDataPath + '/cv/grabcut/mask_probpy.png', mask_prob)
+        if exp_mask1 == None:
+            exp_mask1 = self.scaleMask(mask)
+            cv2.imwrite(self.extraTestDataPath + '/cv/grabcut/exp_mask1py.png', exp_mask1)
+
+        self.assertEqual(self.verify(self.scaleMask(mask), exp_mask1), True)
+
+        mask = mask_prob
+        bgdModel = np.zeros((1,65),np.float64)
+        fgdModel = np.zeros((1,65),np.float64)
+        cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 0, cv2.GC_INIT_WITH_MASK)
+        cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 1, cv2.GC_EVAL)
+
+        if exp_mask2 == None:
+            exp_mask2 = self.scaleMask(mask)
+            cv2.imwrite(self.extraTestDataPath + '/cv/grabcut/exp_mask2py.png', exp_mask2)
+
+        self.assertEqual(self.verify(self.scaleMask(mask), exp_mask2), True)

modules/python/test/test_lk_homography.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+
+'''
+Lucas-Kanade homography tracker test
+===============================
+Uses goodFeaturesToTrack for track initialization and back-tracking for match verification
+between frames. Finds homography between reference and current views.
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+#local modules
+from tst_scene_render import TestSceneRender
+from tests_common import NewOpenCVTests, isPointInRect
+
+lk_params = dict( winSize  = (19, 19),
+                  maxLevel = 2,
+                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
+
+feature_params = dict( maxCorners = 1000,
+                       qualityLevel = 0.01,
+                       minDistance = 8,
+                       blockSize = 19 )
+
+def checkedTrace(img0, img1, p0, back_threshold = 1.0):
+    p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, 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
+
+class lk_homography_test(NewOpenCVTests):
+
+    render = None
+    framesCounter = 0
+    frame = frame0 = None
+    p0 = None
+    p1 = None
+    gray0 = gray1 = None
+    numFeaturesInRectOnStart = 0
+
+    def test_lk_homography(self):
+        self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'),
+            self.get_sample('samples/data/box.png'), noise = 0.1, speed = 1.0)
+
+        frame = self.render.getNextFrame()
+        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        self.frame0 = frame.copy()
+        self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
+
+        isForegroundHomographyFound = False
+
+        if self.p0 is not None:
+            self.p1 = self.p0
+            self.gray0 = frame_gray
+            self.gray1 = frame_gray
+            currRect = self.render.getCurrentRect()
+            for (x,y) in self.p0[:,0]:
+                if isPointInRect((x,y), currRect):
+                    self.numFeaturesInRectOnStart += 1
+
+        while self.framesCounter < 200:
+            self.framesCounter += 1
+            frame = self.render.getNextFrame()
+            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            if self.p0 is not None:
+                p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
+
+                self.p1 = p2[trace_status].copy()
+                self.p0 = self.p0[trace_status].copy()
+                self.gray1 = frame_gray
+
+                if len(self.p0) < 4:
+                    self.p0 = None
+                    continue
+                H, status = cv2.findHomography(self.p0, self.p1, cv2.RANSAC, 5.0)
+
+                goodPointsInRect = 0
+                goodPointsOutsideRect = 0
+                for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
+                    if good:
+                        if isPointInRect((x1,y1), self.render.getCurrentRect()):
+                            goodPointsInRect += 1
+                        else: goodPointsOutsideRect += 1
+
+                if goodPointsOutsideRect < goodPointsInRect:
+                    isForegroundHomographyFound = True
+                    self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6)
+            else:
+                p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
+
+        self.assertEqual(isForegroundHomographyFound, True)

modules/python/test/test_lk_track.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python
+
+'''
+Lucas-Kanade tracker
+====================
+
+Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
+for track initialization and back-tracking for match verification
+between frames.
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+#local modules
+from tst_scene_render import TestSceneRender
+from tests_common import NewOpenCVTests, intersectionRate, isPointInRect
+
+lk_params = dict( winSize  = (15, 15),
+                  maxLevel = 2,
+                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
+
+feature_params = dict( maxCorners = 500,
+                       qualityLevel = 0.3,
+                       minDistance = 7,
+                       blockSize = 7 )
+
+def getRectFromPoints(points):
+
+    distances = []
+    for point in points:
+        distances.append(cv2.norm(point, cv2.NORM_L2))
+
+    x0, y0 = points[np.argmin(distances)]
+    x1, y1 = points[np.argmax(distances)]
+
+    return np.array([x0, y0, x1, y1])
+
+
+class lk_track_test(NewOpenCVTests):
+
+    track_len = 10
+    detect_interval = 5
+    tracks = []
+    frame_idx = 0
+    render = None
+
+    def test_lk_track(self):
+
+        self.render = TestSceneRender(self.get_sample('samples/data/graf1.png'), self.get_sample('samples/data/box.png'))
+        self.runTracker()
+
+    def runTracker(self):
+        foregroundPointsNum = 0
+
+        while True:
+            frame = self.render.getNextFrame()
+            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+            if len(self.tracks) > 0:
+                img0, img1 = self.prev_gray, frame_gray
+                p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
+                p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, 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 = []
+                for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
+                    if not good_flag:
+                        continue
+                    tr.append([(x, y), self.frame_idx])
+                    if len(tr) > self.track_len:
+                        del tr[0]
+                    new_tracks.append(tr)
+                self.tracks = new_tracks
+
+            if self.frame_idx % self.detect_interval == 0:
+                goodTracksCount = 0
+                for tr in self.tracks:
+                    oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1])
+                    newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1])
+                    if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect):
+                        goodTracksCount += 1
+
+                if self.frame_idx == self.detect_interval:
+                    foregroundPointsNum = goodTracksCount
+
+                fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1)
+                fgRate = float(goodTracksCount) / (len(self.tracks) + 1)
+
+                if self.frame_idx > 0:
+                    self.assertGreater(fgIndex, 0.9)
+                    self.assertGreater(fgRate, 0.2)
+
+                mask = np.zeros_like(frame_gray)
+                mask[:] = 255
+                for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]:
+                    cv2.circle(mask, (x, y), 5, 0, -1)
+                p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
+                if p is not None:
+                    for x, y in np.float32(p).reshape(-1, 2):
+                        self.tracks.append([[(x, y), self.frame_idx]])
+
+            self.frame_idx += 1
+            self.prev_gray = frame_gray
+
+            if self.frame_idx > 300:
+                break

modules/python/test/test_mser.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+'''
+MSER detector test
+'''
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+class mser_test(NewOpenCVTests):
+    def test_mser(self):
+
+        img = self.get_sample('cv/mser/puzzle.png', 0)
+        smallImg = [
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0,   0,   0, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255,   0,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0,   0, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+         [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]
+        ]
+        thresharr = [ 0, 70, 120, 180, 255 ]
+        kDelta = 5
+        mserExtractor = cv2.MSER_create()
+        mserExtractor.setDelta(kDelta)
+        np.random.seed(10)
+
+        for i in range(100):
+
+            use_big_image = int(np.random.rand(1,1)*7) != 0
+            invert = int(np.random.rand(1,1)*2) != 0
+            binarize = int(np.random.rand(1,1)*5) != 0 if use_big_image else False
+            blur = int(np.random.rand(1,1)*2) != 0
+            thresh = thresharr[int(np.random.rand(1,1)*5)]
+            src0 = img if use_big_image else np.array(smallImg).astype('uint8')
+            src = src0.copy()
+
+            kMinArea = 256 if use_big_image else 10
+            kMaxArea = int(src.shape[0]*src.shape[1]/4)
+
+            mserExtractor.setMinArea(kMinArea)
+            mserExtractor.setMaxArea(kMaxArea)
+            if invert:
+                cv2.bitwise_not(src, src)
+            if binarize:
+                _, src = cv2.threshold(src, thresh, 255, cv2.THRESH_BINARY)
+            if blur:
+                src = cv2.GaussianBlur(src, (5, 5), 1.5, 1.5)
+            minRegs = 7 if use_big_image else 2
+            maxRegs = 1000 if use_big_image else 15
+            if binarize and (thresh == 0 or thresh == 255):
+                minRegs = maxRegs = 0
+            msers, boxes = mserExtractor.detectRegions(src)
+            nmsers = len(msers)
+            self.assertEqual(nmsers, len(boxes))
+            self.assertLessEqual(minRegs, nmsers)
+            self.assertGreaterEqual(maxRegs, nmsers)

modules/python/test/test_watershed.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+
+'''
+Watershed segmentation
+=========
+
+This program demonstrates the watershed segmentation algorithm
+in OpenCV: watershed().
+
+Usage
+-----
+watershed.py [image filename]
+
+Keys
+----
+  1-7   - switch marker color
+  SPACE - update segmentation
+  r     - reset
+  a     - toggle autoupdate
+  ESC   - exit
+
+'''
+
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+class watershed_test(NewOpenCVTests):
+    def test_watershed(self):
+
+        img = self.get_sample('cv/inpaint/orig.png')
+        markers = self.get_sample('cv/watershed/wshed_exp.png', 0)
+        refSegments = self.get_sample('cv/watershed/wshed_segments.png')
+
+        if img == None or markers == None:
+            self.assertEqual(0, 1, 'Missing test data')
+
+        colors = np.int32( list(np.ndindex(3, 3, 3)) ) * 122
+        cv2.watershed(img, np.int32(markers))
+        segments = colors[np.maximum(markers, 0)]
+
+        if refSegments == None:
+            refSegments = segments.copy()
+            cv2.imwrite(self.extraTestDataPath + '/cv/watershed/wshed_segments.png', refSegments)
+
+        self.assertLess(cv2.norm(segments - refSegments, cv2.NORM_L1) / 255.0, 50)

modules/python/test/tests_common.py

@@ -37,7 +37,7 @@ class NewOpenCVTests(unittest.TestCase):
                     with open(candidate, 'rb') as f:
                         filedata = f.read()
             if filedata is None:
-                filedata = urlopen(NewOpenCVTests.repoUrl + '/' + filename).read()
+                return None#filedata = urlopen(NewOpenCVTests.repoUrl + '/' + filename).read()
             self.image_cache[filename] = cv2.imdecode(np.fromstring(filedata, dtype=np.uint8), iscolor)
         return self.image_cache[filename]
 
@@ -64,10 +64,16 @@ class NewOpenCVTests(unittest.TestCase):
 def intersectionRate(s1, s2):
 
     x1, y1, x2, y2 = s1
-    s1 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
+    s1 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
 
     x1, y1, x2, y2 = s2
-    s2 = [[x1, y1], [x2,y1], [x2, y2], [x1, y2] ]
-    #print(np.array(s2))
-    area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
-    return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
+    s2 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
+
+    area, intersection = cv2.intersectConvexConvex(s1, s2)
+    return 2 * area / (cv2.contourArea(s1) + cv2.contourArea(s2))
+
+def isPointInRect(p, rect):
+    if rect[0] <= p[0] and rect[1] <=p[1] and p[0] <= rect[2] and p[1] <= rect[3]:
+        return True
+    else:
+        return False

modules/python/test/tst_scene_render.py

@@ -13,41 +13,89 @@ defaultSize = 512
 
 class TestSceneRender():
 
-    def __init__(self, bgImg = None, deformation = False, **params):
+    def __init__(self, bgImg = None, fgImg = None, deformation = False, noise = 0.0, speed = 0.25, **params):
         self.time = 0.0
         self.timeStep = 1.0 / 30.0
+        self.foreground = fgImg
         self.deformation = deformation
+        self.noise = noise
+        self.speed = speed
 
         if bgImg != None:
             self.sceneBg = bgImg.copy()
         else:
-            self.sceneBg = np.zeros((defaultSize, defaultSize, 3), np.uint8)
+            self.sceneBg = np.zeros(defaultSize, defaultSize, np.uint8)
 
         self.w = self.sceneBg.shape[0]
         self.h = self.sceneBg.shape[1]
 
+        if fgImg != None:
+            self.foreground = fgImg.copy()
+            self.center = self.currentCenter = (int(self.w/2 - fgImg.shape[0]/2), int(self.h/2 - fgImg.shape[1]/2))
+
+            self.xAmpl = self.sceneBg.shape[0] - (self.center[0] + fgImg.shape[0])
+            self.yAmpl = self.sceneBg.shape[1] - (self.center[1] + fgImg.shape[1])
+
         self.initialRect = np.array([ (self.h/2, self.w/2), (self.h/2, self.w/2 + self.w/10),
          (self.h/2 + self.h/10, self.w/2 + self.w/10), (self.h/2 + self.h/10, self.w/2)]).astype(int)
         self.currentRect = self.initialRect
+        np.random.seed(10)
+
+    def getXOffset(self, time):
+        return int(self.xAmpl*cos(time*self.speed))
+
+
+    def getYOffset(self, time):
+        return int(self.yAmpl*sin(time*self.speed))
 
     def setInitialRect(self, rect):
         self.initialRect = rect
 
+    def getRectInTime(self, time):
+
+        if self.foreground != None:
+            tmp = np.array(self.center) + np.array((self.getXOffset(time), self.getYOffset(time)))
+            x0, y0 = tmp
+            x1, y1 = tmp + self.foreground.shape[0:2]
+            return np.array([y0, x0, y1, x1])
+        else:
+            x0, y0 = self.initialRect[0] + np.array((self.getXOffset(time), self.getYOffset(time)))
+            x1, y1 = self.initialRect[2] + np.array((self.getXOffset(time), self.getYOffset(time)))
+            return np.array([y0, x0, y1, x1])
+
     def getCurrentRect(self):
-        x0, y0 = self.currentRect[0]
-        x1, y1 = self.currentRect[2]
-        return np.array([x0, y0, x1, y1])
+
+        if self.foreground != None:
+
+            x0 = self.currentCenter[0]
+            y0 = self.currentCenter[1]
+            x1 = self.currentCenter[0] + self.foreground.shape[0]
+            y1 = self.currentCenter[1] + self.foreground.shape[1]
+            return np.array([y0, x0, y1, x1])
+        else:
+            x0, y0 = self.currentRect[0]
+            x1, y1 = self.currentRect[2]
+            return np.array([x0, y0, x1, y1])
 
     def getNextFrame(self):
-        self.time += self.timeStep
         img = self.sceneBg.copy()
 
-        self.currentRect = self.initialRect + np.int( 30*cos(self.time) + 50*sin(self.time/3))
-        if(self.deformation):
-            self.currentRect[1:3] += np.int(self.h/20*cos(self.time))
+        if self.foreground != None:
+            self.currentCenter = (self.center[0] + self.getXOffset(self.time), self.center[1] + self.getYOffset(self.time))
+            img[self.currentCenter[0]:self.currentCenter[0]+self.foreground.shape[0],
+             self.currentCenter[1]:self.currentCenter[1]+self.foreground.shape[1]] = self.foreground
+        else:
+            self.currentRect = self.initialRect + np.int( 30*cos(self.time) + 50*sin(self.time/3))
+            if self.deformation:
+                self.currentRect[1:3] += int(self.h/20*cos(self.time))
+            cv2.fillConvexPoly(img, self.currentRect, (0, 0, 255))
 
-        cv2.fillConvexPoly(img, self.currentRect, (0, 0, 255))
+        self.time += self.timeStep
 
+        if self.noise:
+            noise = np.zeros(self.sceneBg.shape, np.int8)
+            cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
+            img = cv2.add(img, noise, dtype=cv2.CV_8UC3)
         return img
 
     def resetTime(self):
@@ -58,7 +106,7 @@ if __name__ == '__main__':
 
     backGr = cv2.imread('../../../samples/data/lena.jpg')
 
-    render = TestSceneRender(backGr)
+    render = TestSceneRender(backGr, noise = 0.5)
 
     while True: