Yaml refactor (#31)

* Updated configuration and experiment yaml files

All the calls to it are now broken.  Working on that next.

* Fixed paths in yaml refactor

I suspect there are more path breaks lurking, but using the --synthetic_hardware call it is now bringing up the GUI.  Available for evaluation @codeCollision4 and @annie-xd-wang.  Hopefully the experiment file that I put up is also clear.  I am available for a discussion if you would like probably around 12:30 PM my time.

* Added documentation for the remote focusing devices

Author: AdvancedImagingUTSW

src/analysis/flatfield.py

@@ -204,13 +204,13 @@ def baseline_drift(images_list,
         ent1 = 1
         _iter = 0
         total_svd = 0
-        converged = False;
+        converged = False
         A1_hat = np.zeros(resized_images.shape)
         E1_hat = np.zeros(resized_images.shape)
         Y1 = 0
 
         while not converged:
-            _iter = _iter + 1;
+            _iter = _iter + 1
             A1_hat = W_idct_hat * A1_coeff + A_offset
 
             # update E1 using l0 norm

src/analysis/image_decorrelation.py

@@ -0,0 +1,378 @@
+import numpy as np
+import numpy.matlib
+from tifffile import imread, imsave
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import pretty_errors
+
+def getDcorrMax(d):
+    '''
+    # ---------------------------------------
+    #
+    # Return the local maxima of the decorrelation function d
+    #
+    # Inputs:
+    #  d        	Decorrelation function
+    #
+    # Outputs:
+    #  ind        	Position of the local maxima
+    #  A			Amplitude of the local maxima
+    #
+    # ---------------------------------------
+    '''
+
+    ind = np.argmax(d)
+    A = d[ind]
+    t = d;
+    # arbitrary peak significance parameter imposed by numerical noise
+    # this becomes relevant especially when working with post-processed data
+    dt = 0.001;
+
+    while ind == np.shape(t)[0]:
+        t[-1] = []
+        if np.isempty(t):
+            A = 0
+            ind = 1
+        else:
+            ind = np.argmax(t)
+            A = t[ind]
+            # check if the peak is significantly larger than the former minimum
+            if t[ind] - np.min(d[ind:-1]) > dt:
+                break
+            else:
+                t[ind] = np.min(d[ind:-1])
+                ind = np.shape(t)[0]
+
+    return ind, A
+
+def getDcorrLocalMax(d):
+    '''
+    # [ind,A] = getDcorrLocalMax(d)
+    # ---------------------------------------
+    #
+    # Return the local maxima of the decorrelation function d
+    #
+    # Inputs:
+    #  d        	Decorrelation function
+    #
+    # Outputs:
+    #  ind        	Position of the local maxima
+    #  A			Amplitude of the local maxima
+    #
+    # ---------------------------------------
+    '''
+    numel = np.shape(d)
+    numel = numel[0]
+
+    if numel < 1:
+        ind = 0
+        A = d[0]
+    else:
+        ind = np.argmax(d)
+        A = d[ind]
+        while numel > 1:
+            # if the last indexed position
+            if ind == numel:
+                d[-1] = []
+                ind = np.argmax(d)
+                A = d[ind]
+
+            # If the first indexed position
+            elif ind == 0:
+                break
+
+            # If the minimum amplitude between ind and the last index is above a threshold
+            elif (A - np.min(d[ind:-1])) >= 0.0005:
+                break
+
+            # If the threshold is not met.
+            else:
+                d[-1] = []
+                ind = np.argmax(d)
+                A = d[ind]
+
+        if ind.size == 0:
+            ind = 0
+            A = d[0]
+
+        else:
+            A = d[ind]
+
+    return ind, A
+
+def linear_map(val, valMin, valMax):
+    '''
+    # rsc = linear_map(val,valMin,valMax,mapMin,mapMax)
+    # ---------------------------------------
+    #
+    # Performs a linear mapping of val from the range [valMin,valMax] to the range [mapMin,mapMax]
+    #
+    # Inputs:
+    #  val        	Input value
+    #  valMin		Minimum value of the range of val
+    #  valMax		Maximum value of the range of val
+    #  mapMin		Minimum value of the new range of val
+    #  mapMax		Maximum value of the new range of val
+    #
+    # Outputs:
+    #  rsc        	Rescaled value
+    #
+    # Example : rsc = linear_map(val,0,255,0,1); # map the uint8 val to the range [0,1]
+    #
+    # ---------------------------------------
+    '''
+
+    mapMin = valMin
+    mapMax = valMax
+    valMin = np.min(val)
+    valMax = np.max(val)
+
+    # convert the input value between 0 and 1
+    tempVal = (val-valMin)/(valMax-valMin)
+
+    # clamp the value between 0 and 1
+    map0 = tempVal < 0
+    map1 = tempVal > 1
+    tempVal[map0] = 0
+    tempVal[map1] = 1
+
+    # rescale and return
+    rsc = np.multiply(tempVal, (mapMax-mapMin)) + mapMin
+    return rsc
+
+def apod_im_rect(image, N):
+    '''
+    # [out,mask] = apodImRect(in,N)
+    # ---------------------------------------
+    #
+    # Apodize the edges of a 2D image
+    #
+    # Inputs:
+    #  in        	Input image
+    #  N            Number of pixels of the apodization
+    #
+    # Outputs:
+    #  out        	Apodized image
+    #  mask         Mask used to apodize the image
+    #
+    # ---------------------------------------
+    '''
+
+    number_x, number_y = np.shape(image)
+
+    x = np.abs(np.linspace(-number_x/2, number_x/2, number_x))
+    y = np.abs(np.linspace(-number_y/2, number_y/2, number_y))
+    mapx = x > (number_x/2 - N)
+    mapy = y > (number_y/2 - N)
+
+    val = np.mean(image)
+
+    d = np.multiply((-np.abs(x) - np.mean(-np.abs(x[mapx]))), mapx)
+    d = linear_map(d, -np.pi/2, np.pi/2)
+    d[mapx == False] = np.pi/2
+    maskx = (np.sin(d)+1)/2
+
+    d = np.multiply((-np.abs(y) - np.mean(-np.abs(y[mapy]))), mapy)
+    d = linear_map(d, -np.pi/2, np.pi/2)
+    d[mapy == False] = np.pi/2
+    masky = (np.sin(d)+1)/2
+
+    # Make it 2D
+    # np.matlib.repmat(a, m, n). a: array/matrix. m,n: int - number of times a is repeated on first and second axes.
+    mask_1 = np.matlib.repmat(masky, number_x, 1)
+    mask_2 = np.matlib.repmat(maskx, number_y, 1)
+    mask_2 = np.transpose(mask_2)
+    mask = np.multiply(mask_1, mask_2)
+
+    out = np.multiply((image-val), mask) + val
+
+    return out
+
+def getCorrcoef(I1, I2, c1=None, c2=None):
+    '''
+    # cc = getCorrcoef(I1,I2,c1,c2)
+    # ---------------------------------------
+    #
+    # Return the normalized correlation coefficient expressed in Fourier space
+    #
+    # Inputs:
+    #  I1        	Complex Fourier transfom of image 1
+    #  I2           Complex Fourier transfom of image 2
+    #
+    # Outputs:
+    #  cc        	Normalized cross-correlation coefficient
+    '''
+
+    if c1 == None:
+        c1 = np.square(np.sqrt(np.sum(np.sum(np.abs(I1)))))
+
+    if c2 == None:
+        c2 = np.square(np.sqrt(np.sum(np.sum(np.abs(I2)))))
+    import warnings
+    warnings.simplefilter("ignore", np.ComplexWarning)
+    a = np.sum(np.real(np.multiply(I1, np.conjugate(I2))))
+    b = c1*c2
+    np.seterr(invalid='ignore')
+    cc = np.divide(a, b)
+    cc = np.double(cc)
+    cc = np.floor(1000*cc)/1000
+    return cc
+
+def getDCorr(im, r=None, Ng=10, figID=0):
+    verbose = True
+    if r is None:
+        r = np.linspace(0, 1, 50)
+
+    if np.shape(r)[0] < 30:
+        r = np.linspace(np.min(r), np.max(r), 30)
+
+        if Ng < 5:
+            Ng = 5
+
+    im = np.single(im)
+
+    # Make size odd numbers only.
+    number_x, number_y = np.shape(im)
+    if np.mod(number_x, 2) == 0:
+        im = np.delete(im, 0, 0)
+    if np.mod(number_y, 2) == 0:
+        im = np.delete(im, 0, 1)
+
+    number_x, number_y = np.shape(im)
+
+    x = np.linspace(-1, 1, number_x)
+    y = np.linspace(-1, 1, number_y)
+    x, y = np.meshgrid(x, y)
+    R = np.sqrt(x**2 + y**2)
+    Nr = np.shape(r)[0]
+
+    # In = Fourier Normalized Image
+    In = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(im)))
+    In = np.divide(In, np.abs(In))
+    In[np.isinf(In)] = 0
+    In[np.isnan(In)] = 0
+    mask0 = R**2 < 1**2
+    In = np.multiply(np.transpose(mask0), In)
+
+    # Ik = Fourier Transform of Image
+    Ik = np.multiply(np.transpose(mask0), np.fft.fftshift(np.fft.fft2(np.fft.fftshift(im))))
+    c = np.sqrt(np.sum(np.multiply(Ik, np.conjugate(Ik))))
+    r0 = np.linspace(r[0], r[-1], Nr)
+
+    d0 = np.zeros(np.shape(r)[0])
+    for k in range(np.shape(r)[0], 0, -1):
+        cc = getCorrcoef(Ik, np.transpose(np.square(R)) < np.multiply(r0[k-1]**2, In), c)
+        if np.isnan(cc):
+            cc = 0
+        d0[k-1] = cc
+
+    ind0, snr0 = getDcorrLocalMax(d0)
+
+    k0 = r[ind0]
+    gMax = 2/r0[ind0]
+
+    if np.isinf(gMax):
+        gMax = max(np.shape(im)[0], np.shape(im)[1])/2
+
+    # Search for the Highest Frequency Peak
+    g = np.hstack((np.shape(im)[0]/4, np.exp(np.linspace(np.log(gMax), np.log(0.15), Ng))))
+    d = np.zeros((Nr, 2*Ng))
+
+    number_iterations = 2
+    kc = np.zeros(np.shape(g)[0]*number_iterations)
+    kc[0] = k0
+
+    SNR = np.zeros(np.shape(g)[0]*number_iterations)
+    SNR[0] = snr0
+
+    ind0 = 0
+
+
+    # Two Step Refinement
+    for refin in range(number_iterations):  # 0, 1.
+        for h in range(np.shape(g)[0]):  # 0..10
+
+            # Fourier Gaussian Filtering
+            Ir = np.multiply(np.transpose(Ik), (1 - np.exp(-2*g[h]*g[h]*R**2)))
+            c = np.sqrt(np.sum(np.abs(Ir)**2))
+
+            for k in range(np.shape(r)[0]-1, ind0-1, -1):  # 49...0
+                mask = R**2 < r[k]**2
+                cc = getCorrcoef(Ir[mask], In[np.transpose(mask)], c)
+                if np.isnan(cc):
+                    cc = 0
+                d[k, h + (Ng * refin) - 1] = cc
+
+            ind, amp = getDcorrLocalMax(d[k:-1, h + (Ng * refin) - 1])
+
+            snr = d[ind, h + (Ng * refin) - 1]
+            ind = ind + k
+
+            kc[h + Ng * refin + 1] = r[ind]
+            SNR[h + Ng * refin + 1] = snr
+
+    print("kc :", kc)
+    print("SNR :", SNR)
+
+
+    return 5, 5
+
+def main_image_decorr():
+    '''
+    https://github.com/Ades91/ImDecorr/blob/master/main_imageDecorr.m
+    '''
+    # Load the Data
+    raw_image = np.array(imread('/Users/S155475/Local/Publication materials/2020-SOLS/Data/MV3Membrane/LateralInsets/DetailVesicleTimepoint30.tif'))
+    raw_image = np.double(raw_image)
+
+    # projected pixel size
+    pps = 5
+
+    Nr = 50
+    Ng = 10
+    r = np.linspace(0, 1, Nr)
+
+    GPU = False
+
+    # Apodize Image Edges with a Cosine Function over 20 pixels
+    image = apod_im_rect(raw_image, 20)
+
+    # Compute Resolution
+    figID = 100
+    if GPU:
+        pass  # [kcMax,A0] = getDcorr(gpuArray(image),r,Ng,figID); gpuDevice(1);
+    else:
+        kcMax, A0 = getDCorr(image, r, Ng, figID)
+
+    print("kcMax :", kcMax, "A0 :", A0)
+
+    # sectorial resolution
+    Na = 8  # number of sectors
+    figID = 101
+    if GPU:
+        pass  # [kcMax,A0] = getDcorrSect(gpuArray(image),r,Ng,Na,figID); gpuDevice(1);
+    else:
+        pass  #kcMax, A0 = getDcorrSect(image,r,Ng,Na,figID)
+
+    # Local resolution map
+    tileSize = 200  # in pixels
+    tileOverlap = 0  # in pixels
+    figId = 103
+
+    if GPU:
+        pass  #  [kcMap,A0Map] = getLocalDcorr(gpuArray(image),tileSize,tileOverlap,r,Ng,figID);gpuDevice(1);
+    else:
+        pass  # kcMap, A0Map = getLocalDcorr(image,tileSize,tileOverlap,r,Ng,figID)
+
+    return kcMax, A0
+
+
+if (__name__ == '__main__'):
+    kcMax, AO = main_image_decorr()
+
+
+
+
+
+
+    print("Guess we are done here")