我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用skimage.morphology.square()。
def convert_new(fname, target_size): print('Processing image: %s' % fname) img = Image.open(fname) blurred = img.filter(ImageFilter.BLUR) ba = np.array(blurred) ba_gray = rgb2gray(ba) val = filters.threshold_otsu(ba_gray) # foreground = (ba_gray > val).astype(np.uint8) foreground = closing(ba_gray > val, square(3)) # kernel = morphology.rectangle(5, 5) # foreground = morphology.binary_dilation(foreground, kernel) labels = measure.label(foreground) properties = measure.regionprops(labels) properties = sorted(properties, key=lambda p: p.area, reverse=True) # draw_top_regions(properties, 3) # return ba bbox = properties[0].bbox bbox = (bbox[1], bbox[0], bbox[3], bbox[2]) cropped = img.crop(bbox) resized = cropped.resize([target_size, target_size]) return np.array(resized)
def convert_new_regions(fname, target_size): print('Processing image: %s' % fname) img = Image.open(fname) blurred = img.filter(ImageFilter.BLUR) ba = np.array(blurred) ba_gray = rgb2gray(ba) val = filters.threshold_otsu(ba_gray) # foreground = (ba_gray > val).astype(np.uint8) foreground = closing(ba_gray > val, square(3)) # kernel = morphology.rectangle(5, 5) # foreground = morphology.binary_dilation(foreground, kernel) labels = measure.label(foreground) properties = measure.regionprops(labels) properties = sorted(properties, key=lambda p: p.area, reverse=True) draw_top_regions(properties, 3) return ba
def convert(fname, target_size): # print('Processing image: %s' % fname) img = Image.open(fname) blurred = img.filter(ImageFilter.BLUR) ba = np.array(blurred) ba_gray = rgb2gray(ba) val = filters.threshold_otsu(ba_gray) # foreground = (ba_gray > val).astype(np.uint8) foreground = closing(ba_gray > val, square(3)) # kernel = morphology.rectangle(5, 5) # foreground = morphology.binary_dilation(foreground, kernel) labels = measure.label(foreground) properties = measure.regionprops(labels) properties = sorted(properties, key=lambda p: p.area, reverse=True) # draw_top_regions(properties, 3) # return ba bbox = properties[0].bbox bbox = (bbox[1], bbox[0], bbox[3], bbox[2]) cropped = img.crop(bbox) resized = cropped.resize([target_size, target_size]) return resized
def convert(fname, target_size): img = Image.open(fname) blurred = img.filter(ImageFilter.BLUR) ba = np.array(blurred) h, w, _ = ba.shape if w > 1.2 * h: left_max = ba[:, : w // 32, :].max(axis=(0, 1)).astype(int) right_max = ba[:, - w // 32:, :].max(axis=(0, 1)).astype(int) max_bg = np.maximum(left_max, right_max) foreground = (ba > max_bg + 10).astype(np.uint8) bbox = Image.fromarray(foreground).getbbox() if bbox is None: print('bbox none for {} (???)'.format(fname)) else: left, upper, right, lower = bbox # if we selected less than 80% of the original # height, just crop the square if right - left < 0.8 * h or lower - upper < 0.8 * h: print('bbox too small for {}'.format(fname)) bbox = None else: bbox = None if bbox is None: bbox = square_bbox(img, fname) cropped = img.crop(bbox) resized = cropped.resize([target_size, target_size]) return np.array(resized)
def square_bbox(img, fname): print("square bbox conversion done for image: %s" % fname) w, h = img.size left = max((w - h) // 2, 0) upper = 0 right = min(w - (w - h) // 2, w) lower = h return (left, upper, right, lower)
def subtract_background_median(z, footprint=19, implementation='scipy'): """Remove background using a median filter. Parameters ---------- footprint : int size of the window that is convoluted with the array to determine the median. Should be large enough that it is about 3x as big as the size of the peaks. implementation: str One of 'scipy', 'skimage'. Skimage is much faster, but it messes with the data format. The scipy implementation is safer, but slower. Returns ------- Pattern with background subtracted as np.array """ if implementation == 'scipy': bg_subtracted = z - ndi.median_filter(z, size=footprint) elif implementation == 'skimage': selem = morphology.square(footprint) # skimage only accepts input image as uint16 bg_subtracted = z - filters.median(z.astype(np.uint16), selem).astype(z.dtype) else: raise ValueError("Unknown implementation `{}`".format(implementation)) return np.maximum(bg_subtracted, 0)
def _coarsenImage(image, f): ''' seems to be a more precise (but slower) way to down-scale an image ''' from skimage.morphology import square from skimage.filters import rank from skimage.transform._warps import rescale selem = square(f) arri = rank.mean(image, selem=selem) return rescale(arri, 1 / f, order=0)
def positionToIntensityUncertaintyForPxGroup(image, std, y0, y1, x0, x1): ''' like positionToIntensityUncertainty but calculated average uncertainty for an area [y0:y1,x0:x1] ''' fy, fx = y1 - y0, x1 - x0 if fy != fx: raise Exception('averaged area need to be square ATM') image = _coarsenImage(image, fx) k = _kSizeFromStd(std) y0 = int(round(y0 / fy)) x0 = int(round(x0 / fx)) arr = image[y0 - k:y0 + k, x0 - k:x0 + k] U = positionToIntensityUncertainty(arr, std / fx, std / fx) return U[k:-k, k:-k]
def whiteout_ramp(image, linecoords): # Dilation enlarge the bright segments and cut them out off the original image imagesection = image[linecoords.object] count = 0 for i in morph.dilation(linecoords.object_matrix, morph.square(10)): whitevalue = measurements.find_objects(i == linecoords.object_value + 1) if whitevalue: whitevalue = whitevalue[0][0] imagesection[count,whitevalue.start:whitevalue.stop] = 255 count +=1 return 0
def __call__(self, img_small): m = morphology.square(self.square_size) img_th = morphology.black_tophat(img_small, m) img_sob = abs(filters.sobel_v(img_th)) img_closed = morphology.closing(img_sob, m) threshold = filters.threshold_otsu(img_closed) return img_closed > threshold
