Merge pull request #39 from open-dynamic-robot-initiative/fkloss/blac…

…k_line_length

style: Use black's default line length (88 characters)

demos/demo_camera.py

@@ -43,9 +43,7 @@ def main() -> int:
 
     try:
         if args.pylon:
-            camera_driver = trifinger_cameras.camera.PylonDriver(
-                args.camera_id
-            )
+            camera_driver = trifinger_cameras.camera.PylonDriver(args.camera_id)
         else:
             camera_id = int(args.camera_id) if args.camera_id else 0
             camera_driver = trifinger_cameras.camera.OpenCVDriver(camera_id)

demos/demo_tricamera.py

@@ -35,14 +35,10 @@ def main() -> None:  # noqa: D103
     camera_names = ["camera60", "camera180", "camera300"]
 
     if args.multi_process:
-        camera_data = trifinger_cameras.tricamera.MultiProcessData(
-            "tricamera", False
-        )
+        camera_data = trifinger_cameras.tricamera.MultiProcessData("tricamera", False)
     else:
         camera_data = trifinger_cameras.tricamera.SingleProcessData()
-        camera_driver = trifinger_cameras.tricamera.TriCameraDriver(
-            *camera_names
-        )
+        camera_driver = trifinger_cameras.tricamera.TriCameraDriver(*camera_names)
         camera_backend = trifinger_cameras.tricamera.Backend(  # noqa: F841
             camera_driver, camera_data
         )

pyproject.toml

@@ -1,6 +1,3 @@
-[tool.black]
-line-length = 79
-
 [tool.pylint.messages_control]
 disable = "C0330, C0326"
 

python/trifinger_cameras/charuco_board_handler.py

@@ -159,9 +159,7 @@ def visualize_board(
         debug_image = image
 
         if charuco_ids is not None:
-            debug_image = cv2.aruco.drawDetectedCornersCharuco(
-                image, charuco_corners
-            )
+            debug_image = cv2.aruco.drawDetectedCornersCharuco(image, charuco_corners)
 
             if rvec is not None and tvec is not None:
                 debug_image = cv2.aruco.drawAxis(
@@ -194,9 +192,7 @@ def detect_board_in_camera_stream(self, device=0):
             ret, frame = cap.read()
 
             charuco_corners, charuco_ids, rvec, tvec = self.detect_board(frame)
-            if self.visualize_board(
-                frame, charuco_corners, charuco_ids, rvec, tvec, 1
-            ):
+            if self.visualize_board(frame, charuco_corners, charuco_ids, rvec, tvec, 1):
                 break
 
         # When everything done, release the capture
@@ -235,9 +231,7 @@ def detect_board_in_image(self, filename, visualize=False):
         charuco_corners, charuco_ids, rvec, tvec = self.detect_board(img)
         if charuco_ids is not None:
             if visualize:
-                self.visualize_board(
-                    img, charuco_corners, charuco_ids, rvec, tvec, 0
-                )
+                self.visualize_board(img, charuco_corners, charuco_ids, rvec, tvec, 0)
 
         if rvec is not None:
             print(json.dumps({"rvec": rvec.tolist(), "tvec": tvec.tolist()}))
@@ -246,9 +240,7 @@ def detect_board_in_image(self, filename, visualize=False):
 
         return rvec, tvec
 
-    def detect_board_in_files(
-        self, files: typing.List[str], visualize: bool = False
-    ):
+    def detect_board_in_files(self, files: typing.List[str], visualize: bool = False):
         """Detect the board in multiple files.
 
         Tries to detect the Charuco board in the given list of image files.
@@ -312,9 +304,7 @@ def calibrate(
         self.camera_matrix = None
         self.dist_coeffs = None
 
-        all_corners, all_ids, image_size = self.detect_board_in_files(
-            files, visualize
-        )
+        all_corners, all_ids, image_size = self.detect_board_in_files(files, visualize)
 
         camera_matrix = np.zeros((3, 3))
         dist_coeffs = np.zeros(4)

scripts/analyze_tricamera_log.py

@@ -38,12 +38,7 @@ def main():
     print("Total duration: {:.1f} seconds".format(duration))
 
     stamps = [
-        np.array(
-            [
-                observation.cameras[c].timestamp
-                for observation in log_reader.data
-            ]
-        )
+        np.array([observation.cameras[c].timestamp for observation in log_reader.data])
         for c in range(3)
     ]
 

scripts/calibrate_cameras.py

@@ -36,9 +36,7 @@ def calibrate_intrinsic_parameters(calibration_data, calibration_results_file):
 
     pattern = os.path.join(calibration_data, "*.png")
     files = glob.glob(pattern)
-    camera_matrix, dist_coeffs, error = handler.calibrate(
-        files, visualize=True
-    )
+    camera_matrix, dist_coeffs, error = handler.calibrate(files, visualize=True)
     camera_info = dict()
     camera_info["camera_matrix"] = dict()
     camera_info["camera_matrix"]["rows"] = 3
@@ -47,9 +45,7 @@ def calibrate_intrinsic_parameters(calibration_data, calibration_results_file):
     camera_info["distortion_coefficients"] = dict()
     camera_info["distortion_coefficients"]["rows"] = 1
     camera_info["distortion_coefficients"]["cols"] = 5
-    camera_info["distortion_coefficients"][
-        "data"
-    ] = dist_coeffs.flatten().tolist()
+    camera_info["distortion_coefficients"]["data"] = dist_coeffs.flatten().tolist()
 
     with open(calibration_results_file, "w") as outfile:
         yaml.dump(
@@ -111,9 +107,7 @@ def config_matrix(data):
     calibration_data["projection_matrix"] = dict()
     calibration_data["projection_matrix"]["rows"] = 4
     calibration_data["projection_matrix"]["cols"] = 4
-    calibration_data["projection_matrix"][
-        "data"
-    ] = projection_matrix.flatten().tolist()
+    calibration_data["projection_matrix"]["data"] = projection_matrix.flatten().tolist()
 
     with open(extrinsic_calibration_filename, "w") as outfile:
         yaml.dump(
@@ -258,9 +252,7 @@ def calibrate_mean_extrinsic_parameters(
         # x-distance of the pattern origin to the world origin
         tx = Dx - dx
         # y-distance of the pattern origin to the world origin
-        ty = (Dy - T2 * np.sin(alphr) - T1 * np.tan(alphr)) * np.cos(
-            alphr
-        ) - dy
+        ty = (Dy - T2 * np.sin(alphr) - T1 * np.tan(alphr)) * np.cos(alphr) - dy
         # z-distance of the pattern origin to the world origin
         tz = (
             T2
@@ -283,9 +275,9 @@ def calibrate_mean_extrinsic_parameters(
 
         # absolute world vectors equivalent to cv2 tvec and rvec:
         tvecW = np.matmul(cv2.Rodrigues(rvec)[0], Tvec) + tvec.T
-        rvecW = cv2.Rodrigues(
-            np.matmul(cv2.Rodrigues(rvec)[0], np.matmul(zMat, xMat))
-        )[0]
+        rvecW = cv2.Rodrigues(np.matmul(cv2.Rodrigues(rvec)[0], np.matmul(zMat, xMat)))[
+            0
+        ]
         #        embed()
 
         projection_matrix[ind, 0:4, 0:4] = utils.rodrigues_to_matrix(rvecW)
@@ -406,22 +398,16 @@ def calibrate_mean_extrinsic_parameters(
     calibration_data["camera_matrix"] = dict()
     calibration_data["camera_matrix"]["rows"] = 3
     calibration_data["camera_matrix"]["cols"] = 3
-    calibration_data["camera_matrix"][
-        "data"
-    ] = camera_matrix.flatten().tolist()
+    calibration_data["camera_matrix"]["data"] = camera_matrix.flatten().tolist()
     calibration_data["distortion_coefficients"] = dict()
     calibration_data["distortion_coefficients"]["rows"] = 1
     calibration_data["distortion_coefficients"]["cols"] = 5
-    calibration_data["distortion_coefficients"][
-        "data"
-    ] = dist_coeffs.flatten().tolist()
+    calibration_data["distortion_coefficients"]["data"] = dist_coeffs.flatten().tolist()
 
     calibration_data["projection_matrix"] = dict()
     calibration_data["projection_matrix"]["rows"] = 4
     calibration_data["projection_matrix"]["cols"] = 4
-    calibration_data["projection_matrix"][
-        "data"
-    ] = projection_matrix.flatten().tolist()
+    calibration_data["projection_matrix"]["data"] = projection_matrix.flatten().tolist()
 
     calibration_data["projection_matrix_std"] = dict()
     calibration_data["projection_matrix_std"]["rows"] = 4
@@ -502,10 +488,7 @@ def main():
         )
 
     elif args.action == "extrinsic_mean":
-        if (
-            not args.calibration_data
-            and not args.intrinsic_calibration_filename
-        ):
+        if not args.calibration_data and not args.intrinsic_calibration_filename:
             raise RuntimeError(
                 "neither calibration_data nor intrinsic_calibration_filename not specified."
             )
@@ -518,14 +501,10 @@ def main():
                 calibration_data = yaml.safe_load(file)
 
             def config_matrix(data):
-                return np.array(data["data"]).reshape(
-                    data["rows"], data["cols"]
-                )
+                return np.array(data["data"]).reshape(data["rows"], data["cols"])
 
             camera_matrix = config_matrix(calibration_data["camera_matrix"])
-            dist_coeffs = config_matrix(
-                calibration_data["distortion_coefficients"]
-            )
+            dist_coeffs = config_matrix(calibration_data["distortion_coefficients"])
         else:
             camera_matrix, dist_coeffs = calibrate_intrinsic_parameters(
                 args.calibration_data, args.extrinsic_calibration_filename

scripts/calibrate_trifingerpro_cameras.py

@@ -75,9 +75,7 @@ def calibrate_intrinsic_parameters(
     camera_info["distortion_coefficients"] = dict()
     camera_info["distortion_coefficients"]["rows"] = 1
     camera_info["distortion_coefficients"]["cols"] = 5
-    camera_info["distortion_coefficients"][
-        "data"
-    ] = dist_coeffs.flatten().tolist()
+    camera_info["distortion_coefficients"]["data"] = dist_coeffs.flatten().tolist()
 
     with open(calibration_results_file, "w") as outfile:
         yaml.dump(
@@ -164,9 +162,7 @@ def calibrate_mean_extrinsic_parameters(
         # x-distance of the pattern origin to the world origin
         tx = Dx - dx
         # y-distance of the pattern origin to the world origin
-        ty = (Dy - T2 * np.sin(alphr) - T1 * np.tan(alphr)) * np.cos(
-            alphr
-        ) - dy
+        ty = (Dy - T2 * np.sin(alphr) - T1 * np.tan(alphr)) * np.cos(alphr) - dy
         # z-distance of the pattern origin to the world origin
         tz = (
             T2
@@ -189,9 +185,9 @@ def calibrate_mean_extrinsic_parameters(
 
         # absolute world vectors equivalent to cv2 tvec and rvec:
         tvecW = np.matmul(cv2.Rodrigues(rvec)[0], Tvec) + tvec.T
-        rvecW = cv2.Rodrigues(
-            np.matmul(cv2.Rodrigues(rvec)[0], np.matmul(zMat, xMat))
-        )[0]
+        rvecW = cv2.Rodrigues(np.matmul(cv2.Rodrigues(rvec)[0], np.matmul(zMat, xMat)))[
+            0
+        ]
         #        embed()
 
         pose_matrix[ind, 0:4, 0:4] = utils.rodrigues_to_matrix(rvecW)
@@ -307,9 +303,7 @@ def calibrate_mean_extrinsic_parameters(
     print("Std proj matrix:")
     print(camera_params.tf_world_to_camera_std)
     print("Rel std proj matrix:")
-    print(
-        camera_params.tf_world_to_camera_std / camera_params.tf_world_to_camera
-    )
+    print(camera_params.tf_world_to_camera_std / camera_params.tf_world_to_camera)
 
     return camera_params
 
@@ -417,9 +411,7 @@ def config_matrix(data):
             return np.array(data["data"]).reshape(data["rows"], data["cols"])
 
         camera_matrix = config_matrix(calibration_data["camera_matrix"])
-        dist_coeffs = config_matrix(
-            calibration_data["distortion_coefficients"]
-        )
+        dist_coeffs = config_matrix(calibration_data["distortion_coefficients"])
     else:
         camera_matrix, dist_coeffs = calibrate_intrinsic_parameters(
             image_files, output_file_full, args.visualize

scripts/check_camera_sharpness.py

@@ -61,9 +61,7 @@ def add_label(
     return image
 
 
-def auto_canny_params(
-    image: np.ndarray, sigma: float = 0.33
-) -> tuple[float, float]:
+def auto_canny_params(image: np.ndarray, sigma: float = 0.33) -> tuple[float, float]:
     median = np.median(image)
     lower = max(0, (1.0 - sigma) * median)
     upper = min(255, (1.0 + sigma) * median)
@@ -152,19 +150,15 @@ def main() -> None:
         max(200, params.canny_threshold1),
         params.set_canny_threshold1,
     )
-    cv2.setTrackbarPos(
-        "Canny threshold 1", window_name, int(params.canny_threshold1)
-    )
+    cv2.setTrackbarPos("Canny threshold 1", window_name, int(params.canny_threshold1))
     cv2.createTrackbar(
         "Canny threshold 2",
         window_name,
         0,
         max(400, params.canny_threshold2),
         params.set_canny_threshold2,
     )
-    cv2.setTrackbarPos(
-        "Canny threshold 2", window_name, int(params.canny_threshold2)
-    )
+    cv2.setTrackbarPos("Canny threshold 2", window_name, int(params.canny_threshold2))
     cv2.createTrackbar(
         "Edge mean threshold",
         window_name,
@@ -209,8 +203,7 @@ def main() -> None:
         add_label(image, args.camera_id, "top")
         add_label(
             image,
-            "Canny mean: %.1f | smoothed: %.1f"
-            % (edges_mean, mean_buffer_mean),
+            "Canny mean: %.1f | smoothed: %.1f" % (edges_mean, mean_buffer_mean),
             "bottom",
         )
 

scripts/detect_aruco_marker.py

@@ -4,9 +4,7 @@
 
 def main():
     # ArUco stuff
-    marker_dict = cv2.aruco.getPredefinedDictionary(
-        cv2.aruco.DICT_APRILTAG_16h5
-    )
+    marker_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_APRILTAG_16h5)
 
     cap = cv2.VideoCapture(0)
     while True:

scripts/overlay_real_and_rendered_images.py

@@ -68,9 +68,7 @@ def main():
     finger.reset_finger_positions_and_velocities([-1.57, 1.57, -2.7] * 3)
 
     # load real images
-    image_dir = (
-        args.image_dir if args.image_dir else args.config_dir[0] / "0001"
-    )
+    image_dir = args.image_dir if args.image_dir else args.config_dir[0] / "0001"
     print("image_dir:", image_dir)
     real_images = [
         cv2.imread(str(image_dir / "camera60.png")),
@@ -94,8 +92,7 @@ def on_trackbar(val):
         sim_images = cameras.get_images()
         # images are rgb --> convert to bgr for opencv
         sim_images = [
-            cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-            for img in sim_images
+            cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) for img in sim_images
         ]
         sim_images = np.hstack(sim_images)
 
@@ -106,9 +103,7 @@ def on_trackbar(val):
 
         cv2.namedWindow(title_window)
         trackbar_name = "Alpha x %d" % SLIDER_MAX
-        cv2.createTrackbar(
-            trackbar_name, title_window, 0, SLIDER_MAX, on_trackbar
-        )
+        cv2.createTrackbar(trackbar_name, title_window, 0, SLIDER_MAX, on_trackbar)
         # Show some stuff
         blend.on_trackbar(0)
 

scripts/record_image_dataset.py

@@ -124,9 +124,7 @@ def main():
         camera_module = trifinger_cameras.camera
         image_saver = SingleImageSaver(args.outdir, args.camera_id)
     elif args.driver == "opencv":
-        camera_driver = trifinger_cameras.camera.OpenCVDriver(
-            int(args.camera_id)
-        )
+        camera_driver = trifinger_cameras.camera.OpenCVDriver(int(args.camera_id))
         camera_module = trifinger_cameras.camera
         image_saver = SingleImageSaver(args.outdir, args.camera_id)
 
@@ -147,9 +145,7 @@ def main():
                 observation = camera_frontend.get_latest_observation()
                 if args.driver == "tri":
                     for i, name in enumerate(camera_names):
-                        image = utils.convert_image(
-                            observation.cameras[i].image
-                        )
+                        image = utils.convert_image(observation.cameras[i].image)
                         cv2.imshow(name, image)
                 else:
                     image = utils.convert_image(observation.image)

scripts/tricamera_log_viewer.py

@@ -37,15 +37,9 @@ def main():
         window_60 = "Image Stream camera60"
         window_180 = "Image Stream camera180"
         window_300 = "Image Stream camera300"
-        cv2.imshow(
-            window_60, utils.convert_image(observation.cameras[0].image)
-        )
-        cv2.imshow(
-            window_180, utils.convert_image(observation.cameras[1].image)
-        )
-        cv2.imshow(
-            window_300, utils.convert_image(observation.cameras[2].image)
-        )
+        cv2.imshow(window_60, utils.convert_image(observation.cameras[0].image))
+        cv2.imshow(window_180, utils.convert_image(observation.cameras[1].image))
+        cv2.imshow(window_300, utils.convert_image(observation.cameras[2].image))
 
         # stop if either "q" or ESC is pressed
         if cv2.waitKey(interval) in [ord("q"), 27]:  # 27 = ESC