Update detection to be more reliable

detect.py

@@ -98,21 +98,16 @@ def detect(calibration_width, img_file, show, quick):
         cv2.imshow("Item Sorter", image)
         cv2.waitKey(0)
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    gray = cv2.GaussianBlur(gray, (5, 5), 0)
-    if show and not quick:
-        cv2.imshow("Item Sorter", gray)
-        cv2.waitKey(0)
+    gray = cv2.GaussianBlur(gray, (7, 7), 0)
 
     # perform edge detection, then perform a dilation + erosion to
     # close gaps in between object edges
     edged = cv2.Canny(gray, 50, 100)
-    if show and not quick:
-        cv2.imshow("Item Sorter", edged)
-        cv2.waitKey(0)
-
+    edged = cv2.dilate(edged, None, iterations=2)
+    edged = cv2.erode(edged, None, iterations=2)
     edged = cv2.dilate(edged, None, iterations=1)
     edged = cv2.erode(edged, None, iterations=1)
-
+    edged = cv2.dilate(edged, None, iterations=1)
     if show and not quick:
         cv2.imshow("Item Sorter", edged)
         cv2.waitKey(0)
@@ -127,7 +122,6 @@ def detect(calibration_width, img_file, show, quick):
     pixelsPerMetric = None
     num = 0
 
-
     # Calibration loop
     for c in cnts:
         # if the contour is not sufficiently large, ignore it
@@ -262,7 +256,8 @@ def detect(calibration_width, img_file, show, quick):
             objtype = "Penny"
             iteml = 0
         else:
-            if circular and near(radius * 2 / pixelsPerMetric, 0.38, 0.03):
+            
+            if circular and near(radius * 2 / pixelsPerMetric, 0.4, 0.03):
                 # Keps nut or spacer
                 objtype = "Spacer"
                 mask = np.zeros(gray.shape, np.uint8)
@@ -270,11 +265,13 @@ def detect(calibration_width, img_file, show, quick):
                 #pixelpoints = np.transpose(np.nonzero(mask))
                 hsv = cv2.cvtColor(orig, cv2.COLOR_BGR2HSV)
                 mean_val = cv2.mean(hsv, mask=mask)
-                #print(str(mean_val[0]))
-                if near(mean_val[0], 47, 5) and near(mean_val[1], 70, 5) and near(mean_val[2], 78, 5):
+                mean_rgb = cv2.mean(orig, mask=mask)
+                if near(mean_rgb[2], 59, 3) and near(mean_val[1], 85, 5): #and near(mean_val[2], 78, 5):
                     objtype = "Keps Nut"
-            if circular and near(radius / pixelsPerMetric, 0.23, 0.02):
+                print(str(mean_rgb[2]) + objtype + str(mean_val[1]))
+            elif circular and near(radius / pixelsPerMetric, 0.23, 0.02):
                 objtype = "Washer"
+            #print(str(radius * 2 / pixelsPerMetric) + objtype)
             epsilon = 3  # 0.02*cv2.arcLength(c,True)
             # print(str(epsilon))
             approx = cv2.approxPolyDP(c, epsilon, True)
@@ -359,7 +356,7 @@ def detect(calibration_width, img_file, show, quick):
         if show and not quick:
             cv2.imshow("Item Sorter", orig)
             #cv2.waitKey(1)
-    if quick:
-        return (list, orig)
-    else:
+    if not quick:
         cv2.waitKey(0)
+    return (list, orig)
+        

run_detect.py

@@ -5,12 +5,12 @@ import cv2
 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 = ("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:
-        items,output = detect.detect(calibration_width, file, True, True)
+        items,output = detect.detect(calibration_width, file, True, False)
         print(str(items))
         if "Penny" in items:
             items.remove("Penny")
@@ -30,7 +30,7 @@ if not video:
 	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('tcpclientsrc port=5001 host=192.168.1.129 ! 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)
     
     # server command for imx135 camera ./video2stdout | gst-launch-1.0 -v fdsrc ! h264parse !  rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=192.168.43.152 port=5001

sort.py

@@ -28,4 +28,4 @@ def sort(input):
 
 def clear():
     write(["Unknown"])
-write(["Unknown"])
+#write(["Unknown"])