我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用scipy.ndimage.filters.uniform_filter()。
def compute_colseps_conv(binary,scale=1.0): """Find column separators by convoluation and thresholding.""" h,w = binary.shape # find vertical whitespace by thresholding smoothed = gaussian_filter(1.0*binary,(scale,scale*0.5)) smoothed = uniform_filter(smoothed,(5.0*scale,1)) thresh = (smoothed<amax(smoothed)*0.1) DSAVE("1thresh",thresh) # find column edges by filtering grad = gaussian_filter(1.0*binary,(scale,scale*0.5),order=(0,1)) grad = uniform_filter(grad,(10.0*scale,1)) # grad = abs(grad) # use this for finding both edges grad = (grad>0.5*amax(grad)) DSAVE("2grad",grad) # combine edges and whitespace seps = minimum(thresh,maximum_filter(grad,(int(scale),int(5*scale)))) seps = maximum_filter(seps,(int(2*scale),1)) DSAVE("3seps",seps) # select only the biggest column separators seps = morph.select_regions(seps,sl.dim0,min=args['csminheight']*scale,nbest=args['maxcolseps']) DSAVE("4seps",seps) return seps
def compute_gradmaps(binary,scale): # use gradient filtering to find baselines boxmap = psegutils.compute_boxmap(binary,scale) cleaned = boxmap*binary DSAVE("cleaned",cleaned) if args['usegause']: # this uses Gaussians grad = gaussian_filter(1.0*cleaned,(args['vscale']*0.3*scale, args['hscale']*6*scale),order=(1,0)) else: # this uses non-Gaussian oriented filters grad = gaussian_filter(1.0*cleaned,(max(4,args['vscale']*0.3*scale), args['hscale']*scale),order=(1,0)) grad = uniform_filter(grad,(args['vscale'],args['hscale']*6*scale)) bottom = ocrolib.norm_max((grad<0)*(-grad)) top = ocrolib.norm_max((grad>0)*grad) return bottom,top,boxmap
def draw_blur_levels(): import matplotlib.pyplot as plt from skimage import io image = io.imread('out/66.png') # 36 for $, 79 for O fig, axes = plt.subplots(nrows=2, ncols=3, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() for blur_level in range(6): blurred = uniform_filter(image, 3.0*blur_level, mode='reflect', cval=0) ax[blur_level].imshow(blurred, cmap='gray', interpolation='nearest') ax[blur_level].set_title(str(blur_level), fontsize=20) plt.show()
def measure(self,line): h,w = line.shape smoothed = filters.gaussian_filter(line,(h*0.5,h*self.smoothness),mode='constant') smoothed += 0.001*filters.uniform_filter(smoothed,(h*0.5,w),mode='constant') self.shape = (h,w) a = argmax(smoothed,axis=0) a = filters.gaussian_filter(a,h*self.extra) self.center = array(a,'i') deltas = abs(arange(h)[:,newaxis]-self.center[newaxis,:]) self.mad = mean(deltas[line!=0]) self.r = int(1+self.range*self.mad) if self.debug: figure("center") imshow(line,cmap=cm.gray) plot(self.center) ginput(1,1000)
def compute_gradmaps(binary, scale, usegauss, vscale, hscale, debug=False): # use gradient filtering to find baselines boxmap = psegutils.compute_boxmap(binary,scale) cleaned = boxmap*binary if debug: debug_show(cleaned, "cleaned") if usegauss: # this uses Gaussians grad = gaussian_filter(1.0*cleaned,(vscale*0.3*scale, hscale*6*scale), order=(1,0)) else: # this uses non-Gaussian oriented filters grad = gaussian_filter(1.0*cleaned, (max(4, vscale*0.3*scale), hscale*scale ), order=(1,0)) grad = uniform_filter(grad, (vscale, hscale*6*scale)) if debug: debug_show(grad, "compute_gradmaps grad") bottom = ocrolib.norm_max((grad<0)*(-grad)) top = ocrolib.norm_max((grad>0)*grad) if debug: debug_show(bottom, "compute_gradmaps bottom") debug_show(top, "compute_gradmaps top") return bottom, top, boxmap
def compute_colseps_mconv(binary,scale=1.0): """Find column separators using a combination of morphological operations and convolution.""" h,w = binary.shape smoothed = gaussian_filter(1.0*binary,(scale,scale*0.5)) smoothed = uniform_filter(smoothed,(5.0*scale,1)) thresh = (smoothed<amax(smoothed)*0.1) DSAVE("1thresh",thresh) blocks = morph.rb_closing(binary,(int(4*scale),int(4*scale))) DSAVE("2blocks",blocks) seps = minimum(blocks,thresh) seps = morph.select_regions(seps,sl.dim0,min=args['csminheight']*scale,nbest=args['maxcolseps']) DSAVE("3seps",seps) blocks = morph.r_dilation(blocks,(5,5)) DSAVE("4blocks",blocks) seps = maximum(seps,1-blocks) DSAVE("5combo",seps) return seps
def rb_dilation(image,size,origin=0): """Binary dilation using linear filters.""" output = zeros(image.shape,'f') filters.uniform_filter(image,size,output=output,origin=origin,mode='constant',cval=0) return array(output>0,'i')
def rb_erosion(image,size,origin=0): """Binary erosion using linear filters.""" output = zeros(image.shape,'f') filters.uniform_filter(image,size,output=output,origin=origin,mode='constant',cval=1) return array(output==1,'i')
def generate_blurred_images(image, blur_factor, levels): images = [] for blur_level in range(levels): #images.append(gaussian_filter(image, blur_factor*blur_level, mode='reflect', cval=0)) blurred = uniform_filter(image, blur_factor*blur_level, mode='reflect') images.append(blurred / (blur_factor+1)) # TODO 'constant', or 'reflect' return images
def compute_colseps_conv(binary, csminheight, maxcolseps, scale=1.0, debug=False): """Find column separators by convoluation and thresholding.""" h,w = binary.shape # find vertical whitespace by thresholding smoothed = gaussian_filter(1.0 * binary, (scale, scale*0.5)) smoothed = uniform_filter(smoothed, (5.0*scale,1)) thresh = (smoothed<np.amax(smoothed)*0.1) if debug: debug_show(thresh, "compute_colseps_conv thresh") # find column edges by filtering grad = gaussian_filter(1.0*binary, (scale, scale*0.5), order=(0,1)) grad = uniform_filter(grad, (10.0*scale,1)) # grad = abs(grad) # use this for finding both edges grad = (grad>0.5*np.amax(grad)) if debug: debug_show(grad, "compute_colseps_conv grad") # combine edges and whitespace seps = np.minimum(thresh,maximum_filter(grad, (int(scale), int(5*scale)))) seps = maximum_filter(seps,(int(2*scale),1)) if debug: debug_show(seps, "compute_colseps_conv seps") # select only the biggest column separators seps = morph.select_regions(seps,sl.dim0, min=csminheight*scale, nbest=maxcolseps) if debug: debug_show(seps, "compute_colseps_conv 4seps") return seps
