从灰度图像，根据阈值，切出多个前景区域，过滤面积太小的图像。
OpenCV的Python逻辑，clip_gray_patches：
def clip_gray_patches(img_gray, ths=32, filter_percent=0.0005):
"""
从灰度图像切出多个前景区域，阈值大于ths，过滤面积占比小于filter_percent的图像
@param img_gray: 灰度图像
@param ths: 前景阈值
@param filter_percent: 过滤面积
@return: patches list, 轮廓图像
"""
# 根据thresh_val过滤mask
ret, gray_mask = cv2.threshold(img_gray, ths, 1, 0)
contours, hierarchy = cv2.findContours(gray_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
img_area = get_image_size(img_gray) # 图像面积
img_copy = copy.copy(img_gray)
img_patches = []
# 遍历全部轮廓
for cnt in contours:
area = cv2.contourArea(cnt)
if area / img_area < filter_percent: # 过滤小图像
continue
# 将小patch的前景设置为255，背景设置为0
mask = np.zeros(img_gray.shape)
cv2.drawContours(mask, [cnt], -1, 255, -1)
mask = mask.astype(np.uint8)
# 将原图，根据mask，贴入新图像中，再提取mask
masked = cv2.add(img_gray, np.zeros(np.shape(img_gray), dtype=np.uint8), mask=mask)
box = get_mask_box(mask)
img_patch = get_cropped_patch(masked, box)
img_patches.append(img_patch)
img_copy = cv2.drawContours(img_copy, [cnt], -1, 255, 1) # 绘制边界
return img_patches, img_copy
def get_image_size(img):
"""
获取图像尺寸
"""
h, w = img.shape[:2]
return float(h * w)
def get_mask_box(mask):
"""
mask的边框
"""
import numpy as np
y, x = np.where(mask)
x_min = np.min(x)
x_max = np.max(x)
y_min = np.min(y)
y_max = np.max(y)
box = [x_min, y_min, x_max, y_max]
return box
def get_cropped_patch(img, box):
"""
获取Img的Patch
:param img: 图像
:param box: [x_min, y_min, x_max, y_max]
:return 图像块
"""
h, w = img.shape[:2]
x_min = int(max(0, box[0]))
y_min = int(max(0, box[1]))
x_max = int(min(box[2], w))
y_max = int(min(box[3], h))
if len(img.shape) == 3:
img_patch = img[y_min:y_max, x_min:x_max, :]
else:
img_patch = img[y_min:y_max, x_min:x_max]
return img_patch
输入的灰度图像：
输出图像：
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