Added options to runfile.

scripts/run.py

@@ -68,6 +68,9 @@ def parse_args():
 
 	parser.add_argument("--sharpen", default=0, help="Set amount of sharpening applied to NeRF training images. Range 0.0 to 1.0.")
 
+	# parser.add_argument("--tomonerf", action="store_true", help="Apply tomography specific parameters to optimise NeRF output.")
+	parser.add_argument("--tomonerf", type=int, default=0, help="Which transforms to apply [single_channel, exponentiate img data, crop].")
+	parser.add_argument("--crop", nargs=3, type=float, help="Percentage to crop unit cube to.")
 
 	return parser.parse_args()
 
@@ -123,6 +126,50 @@ if __name__ == "__main__":
 	elif args.network:
 		testbed.reload_network_from_file(args.network)
 
+	if args.tomonerf:
+		print("***Entering NERF for Tomography mode***")
+
+		if (format(args.tomonerf, 'b')[-1] == '1'):
+			print("TODO: Single channel mode")
+			# single channel
+
+		if (format(args.tomonerf, 'b')[-2] == '1'):
+			print("TODO: Exponentiate data")
+
+		if (format(args.tomonerf, 'b')[-3] == '1'):
+			print("TODO: Optimal cropping")
+
+			# args.gui = True
+			args.width = args.height = 400
+			print("Scaling image to be within unit cube...")
+			# "aabb_scale": 1, # nothing exists outside unit cube
+			# 	with open(args.rendering_params) as f:
+			# 		rendering_params = json.load(f)
+			# 	data_dir=os.path.dirname(args.test_transforms)
+			
+			# TODO: set scale, bounding box
+			# becomes 0.048, multiplied by 0.012 * 
+			# from transforms.json training data, we have:
+			# "scale": 0.012,
+			# TODO: experiment with n_params, n_encoding_params
+
+			# 	# Evaluate metrics on black background
+			# 	testbed.background_color = [0.0, 0.0, 0.0, 1.0]
+			# print(f"Cropbox is {testbed.crop_box()}")
+			crop_x, crop_y, crop_z = args.crop
+
+			# box = np.array([[0.0, 0.0, -2.0, 0.0],[2.0, 0.,0.,0.],[0.,-2.0,  0.0, 0.0]], dtype=np.float32)
+			# testbed.set_crop_box(box)
+			crop_min = [(100-size)/200 for size in args.crop]
+			crop_max = [1-(100-size)/200 for size in args.crop]
+			testbed.render_aabb = ngp.BoundingBox(crop_min, crop_max)
+			print(f"Cropped to min {crop_min}, max {crop_max}")
+
+			# print(f"Testbed options is {dir(testbed)}")
+			# print(f"Testbed.bounding_radius are {dir(testbed.bounding_radius)}")
+	else: 
+		print("Tomonerf: Single channel mode")
+
 	ref_transforms = {}
 	if args.screenshot_transforms: # try to load the given file straight away
 		print("Screenshot transforms from ", args.screenshot_transforms)
@@ -239,13 +286,16 @@ if __name__ == "__main__":
 				testbed.render_ground_truth = False
 				image = testbed.render(resolution[0], resolution[1], spp, True)
 
-				if i == 0:
-					write_image(f"ref.png", ref_image)
-					write_image(f"out.png", image)
+				if True: # i == 0:
+		
+					print(f"Testing against provided ref images")
+					print(f"Rendering GT as ref.png, out.png")
+					write_image(f"ref_{i}.png", ref_image)
+					write_image(f"out_{i}.png", image)
 
 					diffimg = np.absolute(image - ref_image)
 					diffimg[...,3:4] = 1.0
-					write_image("diff.png", diffimg)
+					write_image("diff_{i}.png", diffimg)
 
 				A = np.clip(linear_to_srgb(image[...,:3]), 0.0, 1.0)
 				R = np.clip(linear_to_srgb(ref_image[...,:3]), 0.0, 1.0)