def apply_harris_corners(image):
"""run the harris corner detection function and dilate to increase point
size.
:param img: the loaded image
:type img: cv2 image
:return: copy of the image with the corner detections
:rtype: cv2 image
"""
# copy the image
img = np.copy(image)
# convert to gray before corner detection
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# get the corners
corners = cv2.cornerHarris(gray, 2, 3, 0.04)
# result is dilated for marking the corners, not important
corners = cv2.dilate(corners, None)
# put the corners on the image
img[corners > 0.01 * corners.max()] = [0, 0, 255]
return img
def get_corners(img, param1=2, param2=3, param3=0.04):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, param1, param2, param3)
ret, dst = cv2.threshold(dst, 0.1 * dst.max(), 255, 0)
dst = np.uint8(dst)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1), criteria)
return corners
def harris_corner_detect(image):
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
gray = cv.GaussianBlur(gray, (9, 9), 25) # sigma
# cv.imshow('GS gray', gray)
# dst = cv.cornerHarris(gray, 2, 3, 0.04)
dst = cv.cornerHarris(gray, 9, 19, 0.06)
# 膨胀腐蚀-开操作，去除小的干扰角点
kernel = cv.getStructuringElement(cv.MORPH_RECT, (9, 9))
dst = cv.morphologyEx(dst, cv.MORPH_OPEN, kernel)
# 角点绘制
# image[dst > 0.01 * dst.max()] = [0, 0, 255]
image[dst > 0.01 * dst.max()] = [0, 0, 255]
cv.imshow('image for harris', image)
def simple_harris(in_img):
out_img = in_img
gray_img = cv2.cvtColor(in_img, cv2.COLOR_BGR2GRAY) # 转化灰度图
dst = cv2.cornerHarris(gray_img, 2, 3, 0.04) # 角点检测
dst = cv2.dilate(dst, None) # 膨胀,提升后续图像角点标注的清晰准确度
out_img[dst > 0.01 * dst.max()] = (0, 0, 255) # 画图秒点操作(要求彩色图)
# len_x, len_y = in_img.shape[:2] # 获取图像尺寸
# len_x = round(len_x) # 四舍五入取整
# len_y = round(len_y)
# for y in range(len_y):
# for x in range(len_x):
# if dst[x][y]>0.01*dst.max():
# out_img[x][y] = 255 # 返回满足条件的dst索引值,根据索引值来设置点的颜色
return out_img
def detect_corners(img):
"""
Finds corners of the image with Corner Harris
:param img: img in which to find corners
:return: img with corners drawn
"""
blur = cv2.medianBlur(img, 5)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
# result is dilated for marking the corners, not important
dst = cv2.dilate(dst, None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst > 0.01 * dst.max()] = [0, 0, 255]
def cornerHarris(self):
img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['org_img']))
img = np.float32(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 5, 0.07)
# result is dilated for marking the corners, not important
dst = cv2.dilate(dst, None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst > 0.01 * dst.max()] = [0, 0, 255]
filename = str(randint(1000000000, 9999999999)) + session['org_img']
cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), img)
session['corner_img'] = filename
def corner_extraction(image):
"""
角点检测，没用到
:param image:
:return:
"""
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image.copy()
dst = cv2.cornerHarris(image, 2, 3, 0.04)
print('before dilate size:', dst.shape)
dst = cv2.dilate(dst, None)
print('after dilate size:', dst.shape)
image = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
image[dst > 0.01 * dst.max()] = [0, 0, 255]
return image, dst
def corner(image):
img=image.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
# result is dilated for marking the corners, not important
dst = cv2.dilate(dst, None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst > 0.01 * dst.max()] = [0, 0, 255]
#cv2.imshow('dst', image)
#cv2.imwrite("foo/image_1_c.jpg", img)
#print(img.shape)
return img
from cv2 import cv2 as cv
from matplotlib import pyplot as plt
import numpy as np
filename = 'image/harris.jpeg'
img = cv.imread(filename)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = np.float32(gray)
# 输入图像必须是 float32 ,最后一个参数在 0.04 到 0.05 之间
dst = cv.cornerHarris(gray, 2, 3, 0.04)
#result is dilated for marking the corners, not important
dst = cv.dilate(dst, None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst > 0.01 * dst.max()] = [0, 0, 255]
cv.imshow('dst', img)
if cv.waitKey(0) & 0xff == 27:
cv.destroyAllWindows()
# 转化为灰度图
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 二值化处理
ret, binary = cv.threshold(gray, 90, 255, cv.THRESH_BINARY)
cv_show('img', binary)
# 通过腐蚀去除白色噪声
kernel = np.ones((5, 5), np.uint8)
binary = cv.erode(binary, kernel)
# 平滑处理
binary = cv.medianBlur(binary, 5, 0)
cv_show('img', binary)
# Harris检测之前转换成float32类型
gray = np.float32(binary)
# 这里中间两个参数很重要，一开始没注意到
dst = cv.cornerHarris(gray, 6, 9, 0.04)
dst = cv.dilate(dst, None)
img[dst > 0.0281 * dst.max()] = [0, 0, 255]
# 以二值图形式显示修改的地方
ret, dst = cv.threshold(dst, 0.01 * dst.max(), 255, 0)
cv_show('img', dst)
# 保存图片
cv.imwrite('Tests/28-31_2.jpg', img)
# dst = np.uint8(dst)
# ret, labels, stats, centroids = cv.connectedComponentsWithStats(dst)
# criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 100, 0.001)
# corners = cv.cornerSubPix(gray, np.float32(
import matplotlib.pyplot as plt
import numpy as np
from cv2 import cv2
well = plt.imread('K-016.jpg')
well = cv2.cvtColor(well, cv2.COLOR_BGRA2GRAY)
plt.subplot(151)
plt.title('A')
plt.imshow(well)
harris = cv2.cornerHarris(well, 4, 1, 0.00)
plt.subplot(152)
plt.title('B')
plt.imshow(harris)
x, thr = cv2.threshold(harris, 0.1 * harris.max(), 255, cv2.THRESH_BINARY)
thr = thr.astype('uint8')
plt.subplot(153)
plt.title('C')
plt.imshow(thr)
dst, contours, hierarchy = cv2.findContours(thr.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
areas = map(lambda x: cv2.contourArea(cv2.convexHull(x)), contours)
max_i = areas.index(max(areas))
d = cv2.drawContours(np.zeros_like(thr), contours, max_i, 255, 1)
plt.subplot(154)
plt.title('D')
plt.imshow(d)
rect = cv2.minAreaRect(contours[max_i])
from cv2 import cv2
import numpy as np
file_path = 'src/house.jpg'
img = cv2.imread(file_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 해리스 코너 검출 ---①
corner = cv2.cornerHarris(gray, 2, 3, 0.04)
# 변화량 결과의 최대값 10% 이상의 좌표 구하기 ---②
coord = np.where(corner > 0.1 * corner.max())
coord = np.stack((coord[1], coord[0]), axis=-1)
# 코너 좌표에 동그리미 그리기 ---③
for x, y in coord:
cv2.circle(img, (x, y), 5, (0, 0, 255), 1, cv2.LINE_AA)
# 변화량을 영상으로 표현하기 위해서 0~255로 정규화 ---④
corner_norm = cv2.normalize(corner, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
# 화면에 출력
corner_norm = cv2.cvtColor(corner_norm, cv2.COLOR_GRAY2BGR)
merged = np.hstack((corner_norm, img))
cv2.imshow('Harris Corner', merged)
cv2.waitKey()
cv2.destroyAllWindows()
## 모서리 감지
# cornerHarris = 해리스 모서리 감지
# 두 개의 경계선이 교차하는 지점을 감지하는 방법
# 윈도(이웃, 패치)안 픽셀이 작은 움직임에도 크게 변하는 윈도 찾기
image_bgr = cv2.imread('../img/black.jpg')
image_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
image_gray = np.float32(image_gray)
block_size = 2 # 모서리 감지 매개변수를 설정
aperture = 29
free_parameter = 0.04
# block_size = 각 픽셀에서 모서리 감지에 사용되는 이웃 픽셀 크기
# aperture = 사용하는 소벨 커널 크기
detector_responses = cv2.cornerHarris(image_gray, block_size, aperture,
free_parameter) # 모서리를 감지
detector_responses = cv2.dilate(detector_responses, None) # 모서리 표시를 부각시킵니다.
# 임계값보다 큰 감지 결과만 남기고 흰색으로 표시합니다.
threshold = 0.02
image_bgr[detector_responses > threshold * detector_responses.max()] = [
255, 255, 255
]
image_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) # 흑백으로 변환
plt.imshow(image_gray, cmap="gray"), plt.axis("off") # 이미지 출력
plt.show()
# 가능성이 높은 모서리를 출력합니다.
plt.imshow(detector_responses, cmap='gray'), plt.axis("off")
# corners = cv2.goodFeaturesToTrack(gray,84,0.01,10)
# for i in corners:
# x,y = i.ravel()
# cv2.circle(pointImg,(x,y),3,255,-1)
# plt.imshow(pointImg),plt.show()
kernel = np.ones((5, 5), np.uint8)
cv2.imshow('gray2', gray)
gray = cv2.erode(gray, kernel, 2) # 通过腐蚀运算填补黑块内部的白色空隙
cv2.imshow('gray1', gray)
gray = cv2.threshold(gray, 90, 255, cv2.THRESH_BINARY)[1] # 设置过滤无关边界内容
gray = cv2.GaussianBlur(gray, (3, 3), 0) # 使图像模糊，便于减少无关图像干扰
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04) # 找到Harris角点
dst = cv2.dilate(dst, None) # 膨胀操作
ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0) # 二值化，除去不相关图像
dst = cv2.GaussianBlur(dst, (3, 3), 0) # 使图像模糊，便于减少无关图像干扰
dst = np.uint8(dst)
centroids = cv2.connectedComponentsWithStats(dst)[3] # 寻找连通区域
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001
) # 设置终止条件，迭代30次或移动0.001
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1),
criteria) # cv2.cornerSubPix（）检测十分精细，但不便于显示
res = np.hstack((centroids, corners))
res = np.int0(res)
# cv2.cornerHarris检测出的角点，粗略
pointImg[dst > 0.1012 * dst.max()] = [0, 0, 255]
