def show_image(window_name, img, wait=False): hg.cvStartWindowThread() RESIZABLE = 0 hg.cvNamedWindow(window_name, RESIZABLE) hg.cvShowImage(window_name, img) if wait: print 'show_image: press any key to continue..' cv.highgui.cvWaitKey()
screen.blit(s, loc) brightness = 1.0 contrast = 1.0 shots = 0 # so, here is the main part of the program if __name__ == '__main__': # a small welcome print "OpenCV Python capture video" # first, create the necessary window highgui.cvStartWindowThread() highgui.cvNamedWindow('Camera', highgui.CV_WINDOW_AUTOSIZE) highgui.cvStartWindowThread() highgui.cvNamedWindow('Color Segmentation', highgui.CV_WINDOW_AUTOSIZE) highgui.cvStartWindowThread() highgui.cvNamedWindow('Canny', highgui.CV_WINDOW_AUTOSIZE) # move the new window to a better place highgui.cvMoveWindow('Camera', 10, 10) try: # try to get the device number from the command line device = int(sys.argv[1]) # got it ! so remove it from the arguments del sys.argv[1]
minRadius (Int32) Minimal radius of the circles to search for maxRadius (Int32) Maximal radius of the circles to search for. By default the maximal radius is set to max(image_width, image_height). """ import sys, os from opencv import cv from opencv import highgui print "to use: python houghcircles.py imagefile.jpg minRadius maxRadius" # first, create the necessary window highgui.cvStartWindowThread() highgui.cvNamedWindow('GrayScale', highgui.CV_WINDOW_AUTOSIZE) highgui.cvNamedWindow('Canny', highgui.CV_WINDOW_AUTOSIZE) highgui.cvNamedWindow('Image Display Window', highgui.CV_WINDOW_AUTOSIZE) # move the new window to a better place highgui.cvMoveWindow ('GrayScale', 100, 10) highgui.cvMoveWindow ('Canny', 200, 10) highgui.cvMoveWindow ('Image Display Window', 10, 10) #load image image = highgui.cvLoadImage(sys.argv[1]); #create image arrays
Suggest right-arm valuse are: np.array([0.55,-0.4,1.0]), 0.6, 0.8, 1.3) Note, this increases speed by throwing out points outside likely table area. It also is meant to remove the 'floor' as a possible candidate for table surface fits. ''' pc.truncate_pointcloud_to_voi(np.array([0.55, -0.4, 1.0]), 1, 1, 1.3) print 'finished truncate_pointcloud in top level function' #map polygons after translating ''' Visual to quickly check TF ''' if True: print 'overlay_img soon to be obtained' overlay_img = pc.draw_mapped_laser_into_image(pc.map, pc.pts3d, pc.img) print 'overlay_img obtained' import opencv.highgui as hg hg.cvStartWindowThread() hg.cvNamedWindow('ww', 0) hg.cvShowImage('ww', overlay_img) print 'wait for key - line 264' cv.highgui.cvWaitKey() print 'finsihed showing mapped_laser image' if True: print 'do polygon mapping' pc.do_polygon_mapping() #- ###pc.display_3d('labels')### This is based on accurate polygons. print 'map laser into image again' pc.img_mapped = pc.draw_mapped_laser_into_image(pc.map, pc.pts3d, pc.img) #- #Below: create B & W images corresponding to the artifical 'table edge and center' values we specified.
It also is meant to remove the 'floor' as a possible candidate for table surface fits. ''' pc.truncate_pointcloud_to_voi(np.array([0.55,-0.4,1.0]), 1, 1, 1.3) print 'finished truncate_pointcloud in top level function' #map polygons after translating ''' Visual to quickly check TF ''' if True: print 'overlay_img soon to be obtained' overlay_img = pc.draw_mapped_laser_into_image(pc.map, pc.pts3d, pc.img) print 'overlay_img obtained' import opencv.highgui as hg hg.cvStartWindowThread() hg.cvNamedWindow('ww',0) hg.cvShowImage('ww',overlay_img) print 'wait for key - line 264' cv.highgui.cvWaitKey() print 'finsihed showing mapped_laser image' if True: print 'do polygon mapping' pc.do_polygon_mapping() #- ###pc.display_3d('labels')### This is based on accurate polygons. print 'map laser into image again' pc.img_mapped = pc.draw_mapped_laser_into_image(pc.map, pc.pts3d, pc.img) #- #Below: create B & W images corresponding to the artifical 'table edge and center' values we specified. #Below: REMOVED DIAGNOSTIC IMAGES. cvPolyLine and cvFillPoly were acting up in opencv 2.0