output item list and scan

detect.py

@@ -76,13 +76,13 @@ ap.add_argument("-n", "--number", type=int, required=False,
     args = vars(ap.parse_args())
     args2 = ap.parse_args()"""
 def detect(calibration_width, img_file, show, quick):
-    selected = 2
+    list = []
     #if type(args["number"]) == type(selected):
     #    selected = args["number"]
 
     # load the image, convert it to grayscale, and blur it slightly
     image = None
-    print(str(type(img_file)))
+    #print(str(type(img_file)))
     if str(type(img_file)) == "<class 'numpy.ndarray'>":
         image = img_file.copy()
     else:
@@ -232,7 +232,7 @@ def detect(calibration_width, img_file, show, quick):
         dimA = dA / pixelsPerMetric
         dimB = dB / pixelsPerMetric
 
-        if num == num or show:
+        # Item detection
         area_box = dA * dB
         (x, y), radius = cv2.minEnclosingCircle(c)
         area_contour = cv2.contourArea(c)
@@ -247,14 +247,12 @@ def detect(calibration_width, img_file, show, quick):
         else:
             circular = True
             cv2.circle(orig, (int(x), int(y)), int(radius), (255, 0, 0), 2)
-
         objtype = "Unknown"
         itemw = larger(dimA, dimB)
         itemwr = itemw
         itemwr *= 8
         itemwr = round(itemwr)
         itemwr /= 8
-
         itemh = smaller(dimA, dimB)
         itemhr = itemh
         itemhr *= 16
@@ -300,11 +298,9 @@ def detect(calibration_width, img_file, show, quick):
                 if itemhr == 0.3125 and rectangular:
                     objtype = "Standoff"
                     iteml = sizeStandoff(itemw)
-
                 if itemhr == 0.1875 and rectangular:
                     objtype = "Axle"
                     iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
-
         rows, cols = orig.shape[:2]
         [vx, vy, xx, yy] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
         lefty = int((-xx*vy/vx) + yy)
@@ -324,7 +320,6 @@ def detect(calibration_width, img_file, show, quick):
                      (int(xpos2), int(ypos2)), (255, 127, 0), 2)
         # print(str(iteml))
         # draw the object sizes on the image
-            if show or True:
         # cv2.putText(orig, "{:.5f}in".format(itemhr),
         #	(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
         #	0.65, (255, 255, 255), 2)
@@ -348,7 +343,8 @@ def detect(calibration_width, img_file, show, quick):
         if objtype == "Axle":
             output = "{:.2f}in".format(iteml)
             objname += str(itemwr)
-                print(objname)
+        #print(objname)
+        list.append(objname)
         if circular:
             cv2.putText(orig, output,  # print data
                         (int(x - 25), int(y + radius + 40)
@@ -359,12 +355,11 @@ def detect(calibration_width, img_file, show, quick):
                         (int(xpos2 + 10), int(ypos2 + 40)
                          ), cv2.FONT_HERSHEY_SIMPLEX,
                         0.5, (50, 50, 220), 1)
-
     # show the output image
-                if show:
+        if show and not quick:
             cv2.imshow("Item Sorter", orig)
             #cv2.waitKey(1)
     if quick:
-        return orig
+        return (list, orig)
     else:
         cv2.waitKey(0)

run_detect.py

@@ -5,14 +5,28 @@ from imutils.video import FPS
 calibration_width = 0.75
 image = "img7.jpg"
 images = ("img.jpg", "img2.jpg", "img3.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img8.jpg")
+#images = ("img.jpg", "img2.jpg")
 video = False
 def go():
     for file in images:
-        detect.detect(calibration_width, file, True, False)
+        items,output = detect.detect(calibration_width, file, True, True)
+        print(str(items))
+        if "Penny" in items:
+            items.remove("Penny")
+            itema = items[0]
+            valid = True
+            for item in items:
+                if item != itema:
+                    print("Too many items!")
+                    valid = False
+                    break
+            if valid:
+                print("Found " + itema)
 
 if not video:
 	elapsed_time = timeit.timeit(go, number=1)/1
 	print(elapsed_time)
+
 else :
     #tcp capture = cv2.VideoCapture('udpsrc port=5001  ! gdpdepay !  rtph264depay ! avdec_h264 ! videoconvert ! videorate ! video/x-raw,framerate=5/1 ! appsink', cv2.CAP_GSTREAMER)
     capture = cv2.VideoCapture('udpsrc port=9000 caps="application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264" ! rtph264depay ! avdec_h264 ! videoconvert ! appsink sync=false', cv2.CAP_GSTREAMER)
@@ -32,7 +46,8 @@ else :
         #print('frame')
         if x > 1:
             ret,frame = capture.retrieve()
-            cv2.imshow('Item Sorter', detect.detect(calibration_width, frame, True, True))
+            list,output = detect.detect(calibration_width, frame, True, True)
+            cv2.imshow('Item Sorter', output)
             x = 0
         if cv2.waitKey(1)&0xFF == ord('q'):
             break