very promissing fiducial detection

geometry.py

@@ -16,10 +16,18 @@ modes:
   0x02: update_optical_center
 '''
 import logging
-import numpy as np
-
 _log=logging.getLogger(__name__)
 
+import numpy as np
+import PIL.Image
+try:
+  from scipy import ndimage, signal
+except ImportError as e:
+  _log.warning(e)
+try:
+  import cv2 as cv
+except ImportError as e:
+  _log.warning(e)
 
 class geometry:
 
@@ -255,7 +263,6 @@ class geometry:
     # n     number of images to take in region
     # roi   region of interrest to calculate sharpness
     # mode  mode to calculate sharpness (sum/max-min/hist?  of edge detection in roi)
-    import PIL.Image
     if mot is not None:
       p0=mot.get_rbv()
     else:
@@ -277,7 +284,6 @@ class geometry:
         mot.move_abs(p0)
         return p0
     else:
-      from scipy import ndimage, signal
       if type(cam) == list:
         imgLst=cam
         n=len(imgLst)
@@ -321,6 +327,45 @@ class geometry:
       return p
     pass
 
+  @staticmethod
+  def find_fiducial(cam,sz=(210,210),brd=(20,20)):
+    if type(cam)==str:
+      img=PIL.Image.open(cam)
+      img=np.asarray(img)
+    else:
+      img=cam._pic  # get_image()
+      img16=np.array(img, np.int16)
+
+    fid=np.ones((sz[1]+2*brd[1],sz[0]+2*brd[0]),dtype=np.uint8)*255
+    fid[brd[1]:sz[1]+brd[1],brd[0]:sz[0]+brd[0]]=0
+    mask=np.ones((sz[1]+2*brd[1],sz[0]+2*brd[0]),dtype=np.uint8)*255
+    mask[2*brd[1]:sz[1],2*brd[0]:sz[0]]=0
+    #https://docs.opencv.org/4.5.2/d4/dc6/tutorial_py_template_matching.html
+    #res = cv.matchTemplate(img,fid,cv.TM_CCORR_NORMED )
+    res = cv.matchTemplate(img,fid,cv.TM_CCORR_NORMED,mask=mask)
+
+    h,w=img.shape
+    fh2,fw2=fid.shape
+    fw2//=2
+    fh2//=2
+    mtr=np.ndarray((5,2),np.uint16)
+    corr=np.ndarray((5,),np.float32)
+    for i in range(5):
+      p=np.unravel_index(res.argmax(), res.shape)
+      corr[i]=res[p]
+      mtr[i,:]=p
+      y0=max(p[0]-fh2,0)
+      y1=min(p[0]+fh2,h)
+      x0=max(p[1]-fw2,0)
+      x1=min(p[1]+fw2,w)
+      res[y0:y1,x0:x1]*=.5
+    pos=mtr.mean(0)[::-1]+(fw2,fh2)
+    crm=corr.mean()
+    _log.debug(f'position: {pos} correlation:{crm}')
+    return (pos,crm)
+
+
+
   def pix2pos(self, p, zoom=None):
     # returns the position m(x,y) in meter relative to the optical center at a given zoom level of the pixel p(x,y)
     # if zoom=None, the last zoom value is used
@@ -627,6 +672,8 @@ if __name__=="__main__":
     import glob
     imgLst=sorted(glob.glob("scratch/autofocus2/image*.png"))
     geometry.autofocus(imgLst,None)
+  if args.mode&0x10:
+    geometry.find_fiducial("scratch/fiducial/image001.png")
 
 
 # pix2pos="[[1.0, 200.0, 400.0, 600.0, 800.0], [[[0.001182928623952055, -2.6941995127711305e-05], [-4.043716694634124e-05, -0.0011894314084263825]], [[0.0007955995220142541, -3.175003727901119e-05], [-2.0896601103372113e-05, -0.0008100805094631365]], [[0.00048302539335378367, -1.1661121407652543e-05], [-2.0673746995751222e-05, -0.0004950857899461772]], [[0.00028775903460576395, -1.3762555219494508e-05], [-9.319936861519268e-06, -0.0002889214488565999]], [[0.0001788819256630411, -6.470841493681516e-06], [-2.0336605088889967e-06, -0.0001831131753499113]]]]"