Resize images when exporting to pmvs

We extract features from images of size `feature_process_size`.
PMVS should use images of the same size or smaller. Calibration
won't be precise enough otherwise.

bin/export_pmvs

@@ -8,10 +8,19 @@ import json
 import numpy as np
 
 import opensfm.dataset as dataset
+from opensfm import features
 import opensfm.io as io
 from opensfm.reconstruction import *
 
 
+def opencv_calibration_matrix(width, height, focal):
+    '''Calibration matrix as used by OpenCV and PMVS
+    '''
+    f = focal * max(width, height)
+    return np.matrix([[f, 0, 0.5 * (width - 1)],
+                      [0, f, 0.5 * (height - 1)],
+                      [0, 0, 1.0]])
+
 
 ### Prepare OpenSfM output for dense reconstruction with PMVS
 
@@ -68,35 +77,30 @@ if __name__ == "__main__":
                 fvis.write("%d " % ai)
             fvis.write("\n")
 
-            #### write camera projection matrix to txt/%08d.txt
+            #### radially undistort the original image
+            original_image = data.image_as_array(image)
             camera = reconstruction['cameras'][shot['camera']]
+            original_h, original_w = original_image.shape[:2]
+            K = opencv_calibration_matrix(original_w, original_h, camera['focal'])
+            k1 = camera["k1"]
+            k2 = camera["k2"]
+            undistorted_image = cv2.undistort(original_image, K, np.array([k1, k2, 0, 0]))
 
-            w = camera["width"]
-            h = camera["height"]
-            f = camera["focal"] * max(w, h)
+            #### resize and save the undistorted to visualize/%08d.jpg
+            resized_image = features.resized_image(undistorted_image, data.config)
+            new_image_path = os.path.join(output_path, "visualize", base + ".jpg")
+            cv2.imwrite(new_image_path, resized_image)
 
-            # build that projection matrix that PMVS actually accepts!
+            #### write camera projection matrix to txt/%08d.txt
+            resized_h, resized_w = resized_image.shape[:2]
+            resized_K = opencv_calibration_matrix(resized_w, resized_h, camera['focal'])
             Rt = Rt_from_shot(shot)
-            K = np.matrix([[f, 0, 0.5 * (w - 1)], [0, f, 0.5 * (h - 1)], [0, 0, 1.0]])
-            P = K*Rt
+            P = resized_K * Rt
 
             new_txt = os.path.join(output_path, "txt", base + ".txt")
             with open(new_txt, "w") as f:
                 f.write("CONTOUR\n")
-                for r in np.array(P):
-                    for c in r:
-                        f.write("%f " % c)
-                    f.write("\n")
-
-            #### radially undistort image and copy to visualize/%08d.jpg
-            orig_image = os.path.abspath(data.image_files[image]) #why is dataset.__image_file() method private?
-            new_image = os.path.join(output_path, "visualize", base + ".jpg")
-
-            im = cv2.imread(orig_image)
-            k1 = camera["k1"]
-            k2 = camera["k2"]
-            im = cv2.undistort(im, K, np.array([k1, k2, 0, 0]))
-            cv2.imwrite(new_image, im)
+                np.savetxt(f, P, '%f')
 
         fvis.close()