OpenCV

使用conda安装

conda install opencv

或者使用pip安装

pip install opencv-python

读入图片

读入灰度图：

img = cv2.imread('13.jpg',0)

判断图片是否损坏：

for i, image_path in enumerate(train_image_list):
    test_image = cv2.imread(image_path.encode('utf-8'))
    try:
        test_image.shape
#         print("checked for shape {}".format(test_image.shape))
    except AttributeError:
        print i
        print image_path
        print("shape not found")

或者

im = cv2.imread(roidb[i]['image'])
assert im is not None, \
    'Failed to read image \'{}\''.format(roidb[i]['image'])

画图

cv2.circle(image_,(1251, 2661),5,(255,255,0),2) #(x,y)
cv2.imwrite('test_1.png', image_)

画contour

segm = np.array(bounds).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(image, segm, -1, (0,255,0), 8)

Resize

注意size的顺序是反的

cv2.resize(image, dsize=(width, height), 
                                 interpolation=cv2.INTER_CUBIC)

矩：Moments

图像矩可以计算图像的质心，面积等等。

# 根据图像的矩计算重心 
def find_center(contour):
    M = cv2.moments(contour)
    rx,ry,rw,rh = cv2.boundingRect(contour)
    try:
        cx = int(M['m10'] / M['m00'])
        cy = int(M['m01'] / M['m00'])
        print "ROI centroid=", (cx, cy);
    except ZeroDivisionError:
        cx = rx + int(rw / 2)
        cy = ry + int(rh / 2)
        print "ROI centroid=Unknown, use b-box center=", (cx, cy)
    return cx, cy

获得bbox

注意：输入是int型哦~

label_array = np.array(points, dtype=np.int32) #（n, 2）
x1, y1, w, h = cv2.boundingRect(label_array[:, np.newaxis, :]) # (n, 1, 2)

边缘检测：FindContours

代码示例

ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
    contours,_ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
else:
    _, contours,_ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    cont_areas = [ cv2.contourArea(cont) for cont in contours ]
    idx = np.argmax(cont_areas)  # find the largest contour.
    rx,ry,rw,rh = cv2.boundingRect(contours[idx])

计算面积

area = cv2.contourArea(cnt)

斑点检测：BlobDetector

代码示例

detector = cv2.SimpleBlobDetector()