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Viewing changes to xpresser/opencvfinder.py

  • Committer: Chris Wayne
  • Date: 2012-07-05 13:52:37 UTC
  • mfrom: (10.1.6 gtk3-port-with-cairo)
  • Revision ID: chris.wayne@canonical.com-20120705135237-7f52h9sfxwu279dr
Merging in gtk3 support with simple cv, adding debian packaging

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xpresser/opencvfinder.py
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# You should have received a copy of the GNU Lesser General Public License
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# along with this program.  If not, see <http://www.gnu.org/licenses/>.
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#
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import math
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import time
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import numpy
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import opencv
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import opencv.highgui
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import opencv.adaptors
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import SimpleCV
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from xpresser.imagematch import ImageMatch
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FILTER_MARGIN = 25 # %
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DEBUG_PERFORMANCE = False
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class OpenCVFinder(object):
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    def find(self, screen_image, area_image):
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        matches = self._find(screen_image, area_image, best_match=True)
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        if matches:
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            matches.sort(key=lambda match: -match.similarity)
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            return matches[0]
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        return None
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    def _load_image(self, image):
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        if "opencv_image" not in image.cache:
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            if image.filename is not None:
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                opencv_image = opencv.highgui.cvLoadImage(image.filename)
 
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                opencv_image = SimpleCV.Image(image.filename)
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            elif image.array is not None:
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                # The adaptor function can't deal with the alpha channel.
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                array = image.array[:,:,:3]
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                opencv_image = opencv.adaptors.NumPy2Ipl(array)
 
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                opencv_image = image.array
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            else:
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                raise RuntimeError("Oops. Can't load image.")
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            image.cache["opencv_image"] = opencv_image
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        return image.cache["opencv_image"]
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    def _find(self, screen_image, area_image, best_match=False):
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        if DEBUG_PERFORMANCE:
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            started = time.time()
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        screen = self._load_image(screen_image)
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        area = self._load_image(area_image)
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        if DEBUG_PERFORMANCE:
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            print "LOADING IMAGES: %.5fs" % (time.time()-started)
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        result_width = screen.width - area.width + 1
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        result_height = screen.height - area.height + 1
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        result = opencv.cvCreateImage(opencv.cvSize(result_width, result_height),
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                                      opencv.IPL_DEPTH_32F, 1)
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        if DEBUG_PERFORMANCE:
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            started = time.time()
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        opencv.cvMatchTemplate(screen, area, result, opencv.CV_TM_CCORR_NORMED)
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        if DEBUG_PERFORMANCE:
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            print "MATCHING: %.5fs" % (time.time()-started)
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        result = opencv.adaptors.Ipl2NumPy(result)
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        if DEBUG_PERFORMANCE:
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            started = time.time()
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        matches = []
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        for y, x in numpy.argwhere(result >= area_image.similarity):
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            matches.append(ImageMatch(area_image, x, y, result[y, x]))
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            if best_match and result[y, x] == 1.0:
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                return [matches[-1]]
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        if DEBUG_PERFORMANCE:
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            print "FINDING POSITIONS: %.5fs" % (time.time()-started)
 
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        source = self._load_image(screen_image)
 
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        template = self._load_image(area_image)
 
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        results = []
 
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        matches = source.findTemplate(template, method="CCOEFF_NORM")
 
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        if matches:
 
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            for m in [m for m in matches if m.quality >= area_image.similarity]:
 
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                results.append(
 
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                    ImageMatch(area_image, m.x, m.y, m.quality))
 
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                if best_match and m.quality == 1.0:
 
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                    return [results[-1]]
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        x_margin = int(FILTER_MARGIN/100.0 * area_image.width)
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        y_margin = int(FILTER_MARGIN/100.0 * area_image.height)
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        if DEBUG_PERFORMANCE:
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            started = time.time()
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        matches = self._filter_nearby_positions(matches, x_margin, y_margin)
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        if DEBUG_PERFORMANCE:
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            print "FILTERING: %.5fs" % (time.time()-started)
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        return matches
 
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        results = self._filter_nearby_positions(results, x_margin, y_margin)
 
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        if results:
 
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            results.sort(key=lambda match: -match.similarity)
 
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        return results
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    def _filter_nearby_positions(self, matches, x_margin, y_margin):
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        """Remove nearby positions by taking the best one.
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        Doing this is necessary because around a good match there will
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        likely be other worse matches.
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        """