圖中紅色區域的孔和針的檢測
如果相機像素很大，直接找很慢，可以通過深度學習的方式或模闆比對定位，然後再做檢測
import os
import cv2
import numpy as np
import math
import xml.etree.ElementTree as ET
import imutils
# 檢測針腳位置
def needelCenter_detect(img, bool_needle):
params = cv2.SimpleBlobDetector_Params()
# Setup SimpleBlobDetector parameters.
# print('params')
# print(params)
# print(type(params))
# Filter by Area.
params.filterByArea = True
params.minArea = 100
params.maxArea = 10e3
params.minDistBetweenBlobs = 50
# params.filterByColor = True
params.filterByConvexity = False
# tweak these as you see fit
# Filter by Circularity
params.filterByCircularity = False
params.minCircularity = 0.2
# params.blobColor = 0
# # # Filter by Convexity
# params.filterByConvexity = True
# params.minConvexity = 0.87
# Filter by Inertia
# params.filterByInertia = True
# params.filterByInertia = False
# params.minInertiaRatio = 0.01
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect blobs.
minThreshValue = 110
_, gray = cv2.threshold(gray, minThreshValue, 255, cv2.THRESH_BINARY)
# erosion_size = 3
# # element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * erosion_size + 1, 2 * erosion_size + 1),
# # (erosion_size, erosion_size))
# element = cv2.getStructuringElement(cv2.MORPH_ERODE, (2 * erosion_size + 1, 2 * erosion_size + 1),
# (erosion_size, erosion_size))
# gray = cv2.erode(gray, element, 2)
# cv2.imshow("gray",gray)
# cv2.waitKey()
detector = cv2.SimpleBlobDetector_create(params)
if bool_needle == '0':
gray = 255 - gray
keypoints = detector.detect(gray)
# print(len(keypoints))
# print(keypoints[0].pt[0])
# 如果這兒沒檢測到可能會出錯
if len(keypoints) == 0:
print("沒有檢測到針角坐标，可能需要調整針角斑點檢測參數")
return keypoints
else:
# print(len(keypoints))
# im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (255, 0, 0),
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# if keypoints is not None:
# color_img = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB)
# 畫出圓的圓心
# cv2.circle(color_img, (int(keypoints[0].pt[0]), int(keypoints[0].pt[1])), 5, (0, 255, 0), -1)
# cv2.imshow("color_img",color_img)
# cv2.waitKey()
return keypoints
# 檢測連接配接器圓形位置
def circle_detect(image, bool_needle):
# 灰階化
circle_img = image.copy()
gray = cv2.cvtColor(circle_img, cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(gray, 3)
# 針角圓心坐标
out_x = 0
out_y = 0
# 霍夫變換圓檢測
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10e10, param1=100, param2=30, minRadius=100, maxRadius=500)
# 如果沒檢測到會報錯
# 這種判斷方式過于簡單
if circles is None:
print("沒有檢測到連接配接器外圓")
else:
for circle in circles[0]:
# 圓的基本資訊
# print(circle[2])
# 坐标行列－圓心坐标
out_x = int(circle[0])
out_y = int(circle[1])
# 半徑
r = int(circle[2])
# 在原圖用指定顔色标記出圓的邊界
cv2.circle(circle_img, (out_x, out_y), r, (0, 0, 255), 2)
# # 畫出圓的圓心
cv2.circle(circle_img, (out_x, out_y), 3, (0, 0, 255), -1)
# cv2.namedWindow("circle_img", 2)
# cv2.imshow("circle_img", circle_img)
# cv2.waitKey()
# 記錄外圓坐标
out_xpoint = out_x
out_ypoint = out_y
# 隻框出單個針角的位置區域
step_center = 30
step_rect = 60
out_x -= step_center
out_y -= step_center
needleRect = image[out_y: out_y + step_rect, out_x: out_x + step_rect]
# cv2.namedWindow("needleRect", 2)
# cv2.imshow("needleRect", needleRect)
# cv2.waitKey()
# 根據檢測到的圓形連接配接器中心找針角位置
centerPoint = needelCenter_detect(needleRect, bool_needle)
if len(centerPoint) == 0:
print("調整位置")
return False
else:
# 将針角的坐标原還至原圖
in_x = int(centerPoint[0].pt[0])
in_y = int(centerPoint[0].pt[1])
in_x += out_x
in_y += out_y
# 畫出針角的圓心
cv2.circle(circle_img, (in_x, in_y), 3, (0, 255, 0), -1)
# 計算兩者的距離
# 假設通過标定其一個像素代表0.0056mm
DPI = 0.019696448792778324
dis = math.sqrt(math.pow(out_xpoint - in_x,2) + math.pow(out_ypoint - in_y,2))*DPI
print("兩者互相之間的距離為（mm）:", dis)
# 将計算結果儲存在xml檔案
configFile_xml = "wellConfig.xml"
tree = ET.parse(configFile_xml)
root = tree.getroot()
secondRoot = root.find("distance")
print(secondRoot.text)
secondRoot.text = str(dis)
tree.write("wellConfig.xml")
cv2.namedWindow("image", 2)
cv2.imshow("image",circle_img)
cv2.waitKey()
return True
# 1 路徑寫入xml檔案
# 2 檢測結果也寫入xml
if __name__ == "__main__":
# 從xml檔案中讀取要檢測的圖檔位置
configFile_xml = "wellConfig.xml"
tree = ET.parse(configFile_xml)
root = tree.getroot()
needleImagePath = root.find("needleFilePath")
needleTemplate = root.find("needleMatchFilePath")
holeImagePath = root.find("holeFilePath")
holeTemplate = root.find("holeMatchFilePath")
# 檢測有針的
bool_needle = root.find("needle")
print(bool_needle.text)
if bool_needle.text == '0':
image = cv2.imread(needleImagePath.text)
# 直接從圖像中心選ROI再模闆比對減小時間
h, w = image.shape[:2]
height_y = int(h/3)
height_x = int(w/3)
image = image[height_y:2*height_y, height_x:2*height_x]
template = cv2.imread(needleTemplate.text)
# print("needle")
else:
image = cv2.imread(holeImagePath.text)
# 直接從圖像中心選ROI再模闆比對減小時間
h, w = image.shape[:2]
height_y = int(h/3)
height_x = int(w/3)
image = image[height_y:2*height_y, height_x:2*height_x]
template = cv2.imread(holeTemplate.text)
theight, twidth = template.shape[:2]
# 執行模闆比對，采用的比對方式cv2.TM_SQDIFF_NORMED
result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
# 歸一化處理
cv2.normalize(result, result, 0, 1, cv2.NORM_MINMAX, -1)
# 尋找矩陣（一維數組當做向量，用Mat定義）中的最大值和最小值的比對結果及其位置
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
# 比對值轉換為字元串
# 對于cv2.TM_SQDIFF及cv2.TM_SQDIFF_NORMED方法min_val越趨近與0比對度越好，比對位置取min_loc
# 對于其他方法max_val越趨近于1比對度越好，比對位置取max_loc
strmin_val = str(max_val)
# 繪制矩形邊框，将比對區域标注出來
# min_loc：矩形定點
# (min_loc[0]+twidth,min_loc[1]+theight)：矩形的寬高
# (0,0,225)：矩形的邊框顔色；2：矩形邊框寬度
# cv2.rectangle(image, max_loc, (max_loc[0] + twidth, max_loc[1] + theight), (0, 0, 225), 2)
# 顯示結果,并将比對值顯示在标題欄上
# cv2.namedWindow("MatchResult----MatchingValue=" + strmin_val, 2)
# cv2.imshow("MatchResult----MatchingValue=" + strmin_val, image)
# cv2.waitKey()
roi = image[max_loc[1]:max_loc[1] + theight, max_loc[0]:max_loc[0] + twidth]
# cv2.imshow("roi",roi)
# cv2.waitKey()
if circle_detect(roi, bool_needle.text):
print("檢測成功")
else:
print("檢測失敗") <DOCUMENT content_method="full">
<needleFilePath>images/OneType02/Image_04.jpg</needleFilePath>
<needleMatchFilePath>images/NeedleTemplate.jpg</needleMatchFilePath>
<needle>0</needle>
<holeFilePath>images/OneType03/Image_01.jpg</holeFilePath>
<holeMatchFilePath>images/HoleTemplate.jpg</holeMatchFilePath>
<hole>False</hole>
<distance>0.15383418280030892</distance>
<display>
<url>https://www.baidu.com/</url>
<title>Good</title>
</display>
</DOCUMENT> ![TestLink導出用例轉換工具(XML2Excel)[圖]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs=)