Camera parameter conversion from metashape. Create cameras_from_metas…

…hape.py.

tools/cameras_from_metashape.py

@@ -0,0 +1,129 @@
+import numpy as np
+import xml.etree.ElementTree as ET
+import copy
+import os
+
+# change it to your calibration xml file from Metashape
+xml_file = 'cameras.xml'   
+
+# the folder where the NHR "inf" files to be saved.
+target_path = '.'
+
+class Cameras:
+    def __init__(self):
+        self.K = np.zeros((3,3))
+        self.T = np.zeros((4,4))
+        self.T[3,3] = 1.0
+        self.id = -1
+
+    def set_resolution(self, w, h):
+        w = int(w)
+        h = int(h)
+        self.width = w
+        self.height = h
+
+    def set_intrinsic(self, f, cx, cy, b1, b2):
+        f, cx, cy, b1, b2 = float(f), float(cx), float(cy), float(b1), float(b2)
+        cx = self.width/2  + cx
+        cy = self.height/2 + cy
+        fy = f
+        fx = fy+b1
+        self.K= np.array([ [fx, b2, cx],[0,fy,cy],[0,0,1] ])
+        self.K = self.K.astype(np.float32)
+
+    def set_distort(self, dic):
+
+        if 'k1' in dic:
+            k1 = float(dic['k1'])
+        else:
+            k1 = 0
+        if 'k2' in dic:
+            k2 = float(dic['k2'])
+        else:
+            k2 = 0
+        if 'p1' in dic:
+            p1 = float(dic['p1'])
+        else:
+            p1 = 0
+        if 'p2' in dic:
+            p2 = float(dic['p2'])
+        else:
+            p2 = 0
+        if 'k3' in dic:
+            k3 = float(dic['k3'])
+        else:
+            k3 = 0
+
+        self.distort = np.array([k1,k2,p1,p2,k3])
+        self.distort = [k1,k2,p1,p2,k3]
+
+
+tree = ET.ElementTree(file=xml_file)
+root = tree.getroot()
+cameras_temp = [None]*900
+
+for elem in tree.iter(tag='sensor'):
+
+    cam = Cameras()
+    cali = elem.find('calibration')
+
+    if cali.attrib['class'] == 'initial':
+        continue
+    dic = {}
+    for i in cali.iter():
+        dic[i.tag]=i.text
+
+    if 'cx' in dic:
+        cx = float(dic['cx'])
+    else:
+        cx = 0
+
+    if 'cy' in dic:
+        cy = float(dic['cy'])
+    else:
+        cy = 0
+
+    if 'b1' in dic:
+        b1 = float(dic['b1'])
+    else:
+        b1 = 0
+
+    if 'b2' in dic:
+        b2 = float(dic['b2'])
+    else:
+        b2 = 0
+
+    cam.set_resolution(cali[0].attrib['width'],cali[0].attrib['height'])
+    cam.set_intrinsic(dic['f'],cx,cy,b1,b2)
+
+
+
+    cam.set_distort(dic)
+    cameras_temp[int(elem.attrib['id'])] = cam
+    print(int(elem.attrib['id']))
+
+cameras = []
+for elem in tree.iter(tag='cameras'):
+    cameras = [None]*int(elem.attrib['next_id'])
+    for cam in elem.iter(tag='camera'):
+        sensor_id = int(cam.attrib['sensor_id'])
+        cam_id =  int(cam.attrib['id'])
+        cameras[cam_id] = copy.deepcopy(cameras_temp[sensor_id])
+        T = np.array([ float(i) for i in cam[0].text.split(' ')])
+        T = T.reshape(4,4)
+        cameras[cam_id].T = T
+        cameras[cam_id].id = cam_id
+
+
+with open(os.path.join(target_path,'Intrinsic.inf'), 'w') as f:
+    for i,cam in enumerate(cameras):
+        f.write('%d\n'%i)
+        f.write('%f %f %f\n %f %f %f\n %f %f %f\n' % tuple(cam.K.reshape(9).tolist()))
+        f.write('\n')
+
+
+with open(os.path.join(target_path,'CamPose.inf'), 'w') as f:
+    for i,cam in enumerate(cameras):
+        A = cam.T[0:3,:]
+        tmp = np.concatenate( [A[0:3,2].T, A[0:3,0].T,A[0:3,1].T,A[0:3,3].T])
+        f.write('%f %f %f %f %f %f %f %f %f %f %f %f\n' % tuple(tmp.tolist()))