Add A center dot

Code/Ball_Detection/PyTorch_with_ESPCAM/imageTread_AT.py

@@ -38,30 +38,33 @@ if __name__ == "__main__":
         #print(len(gray_image.shape))
         h,w,_ = frame.shape
 
+        # put a dot in center of the frame
+        cv2.circle(frame, (w//2, h//2), 7, (255, 0, 0), -1)
+
         """
-        If you also want to extract the tag pose, estimate_tag_pose should be set to True 
-        and camera_params ([fx, fy, cx, cy]) 
-        and tag_size (in meters) should be supplied. 
-        The detect method returns a list of Detection objects each having 
-        the following attributes 
+        If you also want to extract the tag pose, estimate_tag_pose should be set to True
+        and camera_params ([fx, fy, cx, cy])
+        and tag_size (in meters) should be supplied.
+        The detect method returns a list of Detection objects each having
+        the following attributes
         (note that the ones with an asterisks are computed only if estimate_tag_pose=True):
         """
 
         """
         So fx and fy are the focal lengths expressed in pixels.
-        Cx and Cy describe the coordinates of the so called principal 
-        point that should be in the center of the image. 
-        It is e.g. not in the center of the image if you cropped the image, 
+        Cx and Cy describe the coordinates of the so called principal
+        point that should be in the center of the image.
+        It is e.g. not in the center of the image if you cropped the image,
         what you should never do when calibrating.
-        
-        fx, fy, cx, cy are given in Pixels in Computer Vision ( and openCV) 
+
+        fx, fy, cx, cy are given in Pixels in Computer Vision ( and openCV)
         but e.g. in Photogrammetry you often use mm
         """
         fx = 800
         fy = 600
-        cx = 0
-        cy = 0
-        results = detector.detect(gray_image,estimate_tag_pose=True,camera_params=[fx, fy, cx, cy],tag_size=0.16)
+        cx = 400
+        cy = 300
+        results = detector.detect(gray_image,estimate_tag_pose=True, camera_params=[fx, fy, cx, cy], tag_size=0.16)
 
         # loop over the AprilTag detection results
         for r in results:
@@ -84,10 +87,14 @@ if __name__ == "__main__":
             cv2.circle(frame, (cX, cY), 5, (0, 0, 255), -1)
             # draw the tag family on the image
             print("cX,cY:{},{}".format(cX,cY))
+
+
             tagFamily = r.tag_family.decode("utf-8")
             tid = r.tag_id
-            cv2.putText(frame, tagFamily, (ptA[0], ptA[1] - 15),
-            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            cv2.putText(frame, tagFamily, (ptA[0], ptA[1] - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+            cv2.putText(frame, "tx: {:.2f}  ty: {:.2f}  tz:{:.2f}".format(tx[0],ty[0],tz[0]), (ptA[0], ptA[1] + 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
             print("[INFO] tag id: {}".format(tid))
 
         # show the output image after AprilTag detection