Multi-threaded rectify

contrib/orthorectify/orthorectify.py

@@ -1,8 +1,12 @@
 #!/usr/bin/env python3
-import rasterio
 import os
+import sys
+sys.path.insert(0, os.path.join("..", "..", os.path.dirname(__file__)))
+
+import rasterio
 import numpy as np
 from opensfm import dataset
+import multiprocessing
 
 # TODO: command argument parser
 
@@ -12,6 +16,7 @@ dem_path = "/datasets/brighton2/odm_meshing/tmp/mesh_dsm.tif"
 target_images = [] # all
 target_images.append("DJI_0018.JPG")
 
+
 # Read DSM
 print("Reading DSM: %s" % dem_path)
 with rasterio.open(dem_path) as dem_raster:
@@ -26,52 +31,59 @@ with rasterio.open(dem_path) as dem_raster:
     if len(reconstructions) == 0:
         raise Exception("No reconstructions available")
 
+    max_workers = multiprocessing.cpu_count()
+    print("Using %s threads" % max_workers)
+
     reconstruction = reconstructions[0]
     for shot in reconstruction.shots.values():
         if len(target_images) == 0 or shot.id in target_images:
 
-            print("Loading %s..." % shot.id)
+            print("Processing %s..." % shot.id)
             shot_image = udata.load_undistorted_image(shot.id)
 
             r = shot.pose.get_rotation_matrix()
             Xs, Ys, Zs = shot.pose.get_origin()
             cam_w = shot.camera.width
             cam_h = shot.camera.height
+            img_w, img_h, num_bands = shot_image.shape
             f = shot.camera.focal
 
-            num_bands = shot_image.shape[2]
 
-            imgout = np.full((num_bands, w, h), np.nan, dtype=shot_image.dtype)
+            def process_pixels(step):
+                imgout = np.full((num_bands, w, h), np.nan, dtype=shot_image.dtype)
+                for i in range(w):
+                    if i % max_workers == step:
+                        for j in range(h):
+                            # i, j = (168, 442) # TODO REMOVE
 
+                            # World coordinates
+                            Xa, Ya = dem_raster.xy(i, j)
+                            Za = dem[j][i]
+
+                            # Colinearity function http://web.pdx.edu/~jduh/courses/geog493f14/Week03.pdf
+                            dx = (Xa - Xs)
+                            dy = (Ya - Ys)
+                            dz = (Za - Zs)
+
+                            den = r[0][2] * dx + r[1][2] * dy + r[2][2] * dz
+                            x = 0.5 + (-f * (r[0][0] * dx + r[1][0] * dy + r[2][0] * dz) / den)
+                            y = 0.5 + (-f * (r[0][1] * dx + r[1][1] * dy + r[2][1] * dz) / den)
+
+                            # TEST
+                            if x >= 0 and y >= 0 and x <= 1.0 and y <= 1.0:
+                                for b in range(3):
+                                    imgout[b][i][j] = 255
+                return imgout
+
+            with multiprocessing.Pool(max_workers) as p:
+                results = p.map(process_pixels, range(max_workers))
+
+            # Merge
+            imgout = results[0]
             for i in range(w):
-                for j in range(h):
+                for b in range(num_bands):
+                    imgout[b][i] = results[i % max_workers][b][i]
 
-                    # i, j = (168, 442) # TODO REMOVE
-
-                    # World coordinates
-                    Xa, Ya = dem_raster.xy(j, i)
-                    Za = dem[j][i]
-
-                    # Colinearity function http://web.pdx.edu/~jduh/courses/geog493f14/Week03.pdf
-                    dx = (Xa - Xs)
-                    dy = (Ya - Ys)
-                    dz = (Za - Zs)
-
-                    den = r[0][2] * dx + r[1][2] * dy + r[2][2] * dz
-                    x = 0.5 + (-f * (r[0][0] * dx + r[1][0] * dy + r[2][0] * dz) / den)
-                    y = 0.5 + (-f * (r[0][1] * dx + r[1][1] * dy + r[2][1] * dz) / den)
-
-                    # TODO: interpolate?
-                    # xi = round(x * cam_w)
-                    # yi = round(y * cam_h)
-
-                    for b in range(3):
-
-                        # TEST
-                        if x > 0 and y > 0:
-                            imgout[b][i][j] = 255
-            
-            imgout[imgout == np.nan] = 0
             print(w)
             print(h)
 
@@ -86,4 +98,3 @@ with rasterio.open(dem_path) as dem_raster:
             with rasterio.open("/datasets/brighton2/odm_meshing/tmp/out.tif", 'w', **profile) as wout:
                 for b in range(num_bands):
                     wout.write(imgout[b], b + 1)
-