本文整理汇总了Python中scipy.ndimage.filters.rank_filter方法的典型用法代码示例。如果您正苦于以下问题：Python filters.rank_filter方法的具体用法？Python filters.rank_filter怎么用？Python filters.rank_filter使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.ndimage.filters的用法示例。
在下文中一共展示了filters.rank_filter方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: reduce_noise_edges
# 需要导入模块: from scipy.ndimage import filters [as 别名]
# 或者: from scipy.ndimage.filters import rank_filter [as 别名]
def reduce_noise_edges(im):
structuring_element = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
opening = cv2.morphologyEx(im, cv2.MORPH_OPEN, structuring_element)
maxed_rows = rank_filter(opening, -4, size=(1, 20))
maxed_cols = rank_filter(opening, -4, size=(20, 1))
debordered = np.minimum(np.minimum(opening, maxed_rows), maxed_cols)
return debordered 示例2: process_image
# 需要导入模块: from scipy.ndimage import filters [as 别名]
# 或者: from scipy.ndimage.filters import rank_filter [as 别名]
def process_image(path, out_path):
orig_im = Image.open(path)
scale, im = downscale_image(orig_im)
edges = cv2.Canny(np.asarray(im), 100, 200)
# TODO: dilate image _before_ finding a border. This is crazy sensitive!
_, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
borders = find_border_components(contours, edges)
borders.sort(key=lambda (i, x1, y1, x2, y2): (x2 - x1) * (y2 - y1))
border_contour = None
if len(borders):
border_contour = contours[borders[0][0]]
edges = remove_border(border_contour, edges)
edges = 255 * (edges > 0).astype(np.uint8)
# Remove ~1px borders using a rank filter.
maxed_rows = rank_filter(edges, -4, size=(1, 20))
maxed_cols = rank_filter(edges, -4, size=(20, 1))
debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols)
edges = debordered
contours = find_components(edges)
if len(contours) == 0:
print '%s -> (no text!)' % path
return
crop = find_optimal_components_subset(contours, edges)
crop = pad_crop(crop, contours, edges, border_contour)
crop = [int(x / scale) for x in crop] # upscale to the original image size.
text_im = orig_im.crop(crop)
text_im.save(out_path)
return out_path 示例3: process_image
# 需要导入模块: from scipy.ndimage import filters [as 别名]
# 或者: from scipy.ndimage.filters import rank_filter [as 别名]
def process_image(path, out_path):
orig_im = Image.open(path)
scale, im = downscale_image(orig_im)
edges = cv2.Canny(np.asarray(im), 100, 200)
# TODO: dilate image _before_ finding a border. This is crazy sensitive!
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
borders = find_border_components(contours, edges)
borders.sort(key=lambda (i, x1, y1, x2, y2): (x2 - x1) * (y2 - y1))
border_contour = None
if len(borders):
border_contour = contours[borders[0][0]]
edges = remove_border(border_contour, edges)
edges = 255 * (edges > 0).astype(np.uint8)
# Remove ~1px borders using a rank filter.
maxed_rows = rank_filter(edges, -4, size=(1, 20))
maxed_cols = rank_filter(edges, -4, size=(20, 1))
debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols)
edges = debordered
contours = find_components(edges)
if len(contours) == 0:
print '%s -> (no text!)' % path
return
crop = find_optimal_components_subset(contours, edges)
crop = pad_crop(crop, contours, edges, border_contour)
crop = [int(x / scale) for x in crop] # upscale to the original image size.
#draw = ImageDraw.Draw(im)
#c_info = props_for_contours(contours, edges)
#for c in c_info:
# this_crop = c['x1'], c['y1'], c['x2'], c['y2']
# draw.rectangle(this_crop, outline='blue')
#draw.rectangle(crop, outline='red')
#im.save(out_path)
#draw.text((50, 50), path, fill='red')
#orig_im.save(out_path)
#im.show()
text_im = orig_im.crop(crop)
text_im.save(out_path)
print '%s -> %s' % (path, out_path) 示例4: process_image
# 需要导入模块: from scipy.ndimage import filters [as 别名]
# 或者: from scipy.ndimage.filters import rank_filter [as 别名]
def process_image(path, out_path):
orig_im = Image.open(path)
scale, im = downscale_image(orig_im)
edges = cv2.Canny(np.asarray(im), 100, 200)
# TODO: dilate image _before_ finding a border. This is crazy sensitive!
contours, hierarchy = cv2.findContours(edges,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
borders = find_border_components(contours, edges)
borders.sort(key=lambda (i, x1, y1, x2, y2): (x2 - x1) * (y2 - y1))
border_contour = None
if len(borders):
border_contour = contours[borders[0][0]]
edges = remove_border(border_contour, edges)
edges = 255 * (edges > 0).astype(np.uint8)
# Remove ~1px borders using a rank filter.
maxed_rows = rank_filter(edges, -4, size=(1, 20))
maxed_cols = rank_filter(edges, -4, size=(20, 1))
debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols)
edges = debordered
contours = find_components(edges)
if len(contours) == 0:
print '%s -> (no text!)' % path
return
crop = find_optimal_components_subset(contours, edges)
crop = pad_crop(crop, contours, edges, border_contour)
# upscale to the original image size.
crop = [int(x / scale) for x in crop]
# draw = ImageDraw.Draw(im)
# c_info = props_for_contours(contours, edges)
# for c in c_info:
# this_crop = c['x1'], c['y1'], c['x2'], c['y2']
# draw.rectangle(this_crop, outline='blue')
# draw.rectangle(crop, outline='red')
# im.save(out_path)
# draw.text((50, 50), path, fill='red')
# orig_im.save(out_path)
# im.show()
text_im = orig_im.crop(crop)
text_im.save(out_path)
print '%s -> %s' % (path, out_path)