Improved bolt labels and added pmt-id labels on output mask

find_centroids.py

@@ -2,6 +2,8 @@
 import numpy as np
 from skimage import color, img_as_ubyte
 import os, sys
+import math
+#find_centroids("B.png")
 
 def find_centroids(img_path) :
 
@@ -17,19 +19,14 @@ def find_centroids(img_path) :
     pmts_gray = cv2.inRange(train_labels, lower_pmts, upper_pmts)
     bolts_gray = cv2.inRange(train_labels, lower_bolts, upper_bolts)
     img = cv2.cvtColor(bolts_gray,cv2.COLOR_GRAY2BGR)
-    cv2.imwrite("pmts_gray--4.png",pmts_gray)
-    cv2.imwrite("bolts_gray--4.png",bolts_gray)
-    cv2.imwrite("img--4.png",img)
+    #cv2.imwrite("pmts_gray--4.png",pmts_gray)
+    #cv2.imwrite("bolts_gray--4.png",bolts_gray)
+    #cv2.imwrite("img--4.png",img)
 
 
 
 
 
-
-
-
-    #print('images made')
-
     #Calls HoughCircles() to find multiple PMTs in an image
 
     #Outpts list of circle parameters
@@ -44,9 +41,9 @@ def find_pmts(gray_img) :
                                minRadius=100,
                                 maxRadius=200
                               )
-        #print(circles)
 
-        ##Error over here vvvv, because circles is empty.
+
+
         circles = np.uint16(np.around(circles))
         img2 = cv2.cvtColor(edges,cv2.COLOR_GRAY2BGR)
 
@@ -67,7 +64,7 @@ def ret_centres(image, params) :
         x = params[1]
         y = params[0]
         r = params[2]
-        L = int(r*1.1)
+        L = int(r*1.5)
         gray_image = image[x-L:x+L, y-L:y+L]
         ret,thresh = cv2.threshold(gray_image,127,255,0)
         gray_image = color.gray2rgb(img_as_ubyte(gray_image))
@@ -89,39 +86,134 @@ def ret_centres(image, params) :
                 cX = int(M["m10"] / M["m00"])
                 cY = int(M["m01"] / M["m00"])
                 out.append([cY + (x-L), cX + (y-L)])
-             #   print(area)
 
             else:
                 continue
         return out
     ################################################################
-
+
+
+
+
+    ################################################################
+    # From a list of bolt positions and circle parameters, orders bolts in clockwise order starting from top over 360 degrees
+
+    #returns a list of bolts in clockwise order 
+    def order_bolts(circle, bolt_ring) :
+
+        out = []
+
+        #circle parameters
+        circ_x = circle[0]
+        circ_y = circle[1]
+        circ_r = circle[2]
+        L = int(circ_r*1)  
+
+        #bolt locations found beforehand
+        nodes = np.asarray(bolt_ring)
+
+        #loop over 2pi in (60 for now) steps finding closest bolts
+        for i in np.linspace(0, 2*3.14159, num=60) : 
+
+            #point on circle (scaled by radius of hough transform) to search near
+            search_x = int(circ_x + L*np.sin(i))
+            search_y = int(circ_y - L*np.cos(i))
+
+            #find bolt closest to point
+            dist = np.sum((nodes - [search_y,search_x])**2, axis=1)
+            closest = np.argmin(dist)
+
+            #only append each bolt coordinate once
+            if bolt_ring[closest] not in out :
+                out.append(bolt_ring[closest])
+
+        return out
+
+    ################################################################
 
 
 
 
     #find circles in image
     circles = find_pmts(pmts_gray)
-    bolt_centres = []
+
 
     #loop over circles and find bolts, end up with list of circles, each a list of bolts (no circle parameters are saved )
     for i in circles[0, :] :
         bolt_ring = ret_centres(bolts_gray, i)
-        bolt_centres.append(bolt_ring)
-
+        bolts_ord = order_bolts(i, bolt_ring)
+        pmtID = input("Input PMT number:\n-->")
         #Draw hough circles on img
         cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
         cv2.circle(img,(i[0],i[1]),2,(0,255,0),3)
 
+        #draw bolt locations and number them 
+        bolt_no = 0
+        #print("I is ",i)
+        cv2.putText(img, pmtID, (i[0]-47, i[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,255), 1)
+
+        ######################### Automatic bolt number labelling on mask ##############################
+        radius = i[2]
+        for j in bolts_ord :
+            bolt_no += 1
+
+            #Find the angle at the centre of the PMT between the x axis and 
+            #the line pointing from the centre of the PMT to the centre of the bolt
+            lengthx = j[1]-i[0]
+            lengthy = j[0]-i[1]
+            length = math.sqrt(lengthx**2+lengthy**2)
+            if(lengthx==0):
+                angle=3.1415926/2
+            else:
+                angle = np.arctan(lengthy/lengthx)
+
+            #Calculate text location based on this angle and the location of the bolt along the circle.
+
+            #bolt 1
+            if(bolt_no==1):
+                textx = int(j[1])
+                texty = int(j[0]-(32))
+            #bolts 2 through 14 (on the right side of the circle/the 1st and 4th quadrant) 
+            elif(bolt_no<=12):
+                textx = int(j[1]+(28)*np.cos(angle))
+                texty = int(j[0]+(28)*np.sin(angle))
+            #bolt 13
+            elif(bolt_no==13):
+                textx = int(j[1]+abs((34)*np.cos(angle)))
+                texty = int(j[0]+abs((34)*np.sin(angle)))
+            #bolts 14 through 19 (on the 3rd quadrant)
+            elif(bolt_no<=19):
+                textx = int(j[1]-(40)*np.cos(angle))
+                texty = int(j[0]-(40)*np.sin(angle))
+            #bolts 20 through 24 (on the 2nd quadrant)
+            else:
+                textx = int(j[1]-(37)*np.cos(angle))
+                texty = int(j[0]-(37)*np.sin(angle))
+            pointerx = int((textx+textx+20)/2)
+            pointery = int((texty+texty-20)/2)
+            cv2.line(img, (j[1],j[0]), (pointerx,pointery), (0,177,177), thickness=1, lineType=8, shift=0)
+
+            cv2.rectangle(img,(textx,texty),(textx+15,texty-15),(0,0,0), thickness=-1, lineType=8, shift=0)
+            #cv2.circle(img,(textx,texty),10,(0,0,0),-1)
+            cv2.circle(img, (j[1], j[0]), 5, (0,0,255), -1)
+            cv2.putText(img, f'{bolt_no}', (textx, texty), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,255), 1)
+        ######################### Automatic bolt number labelling on mask ##############################
+
 
     #draw bolt locations
-    for i in bolt_centres :
-        for j in i :
-            cv2.circle(img, (j[1], j[0]), 5, (0,0,255), -1)
 
 
-    print(bolt_centres)
+        #for i in bolts_ord :
+            print(j[0],j[1])
+
+
+
     print(circles)
     print("SAVING")
-    cv2.imwrite('out.png', img )
-
+
+    base = os.path.basename(img_path)
+    filename = os.path.splitext(base)[0]
+    outputMaskName = os.path.join(filename+'-labelled.png')
+    print("Saving labelled output mask to: ",outputMaskName)
+    #save output mask in code directory
+    cv2.imwrite(outputMaskName, img)