style: apply flake8 code style fixes

anylabeling/app.py

@@ -12,6 +12,7 @@
 import logging
 
 import sys
+
 sys.path.append(".")
 
 import yaml
@@ -125,7 +126,7 @@ def main():
         help="epsilon to find nearest vertex on canvas",
         default=argparse.SUPPRESS,
     )
-    args = parser.parse_args()        
+    args = parser.parse_args()
 
     if hasattr(args, "flags"):
         if os.path.isfile(args.flags):
@@ -155,7 +156,9 @@ def main():
     config_file_or_yaml = config_from_args.pop("config")
     logger_level = config_from_args.pop("logger_level")
     logger.setLevel(getattr(logging, logger_level.upper()))
-    logger.info(f"🚀 {gradient_text(f'X-AnyLabeling v{__version__} launched!')}")
+    logger.info(
+        f"🚀 {gradient_text(f'X-AnyLabeling v{__version__} launched!')}"
+    )
     logger.info(f"⭐ If you like it, give us a star: {__url__}")
     anylabeling_config.current_config_file = config_file_or_yaml
     config = get_config(config_file_or_yaml, config_from_args, show_msg=True)

anylabeling/app_info.py

@@ -2,4 +2,4 @@
 __appdescription__ = "Advanced Auto Labeling Solution with Added Features"
 __version__ = "2.4.2"
 __preferred_device__ = "CPU"  # GPU or CPU
-__url__ = "https://github.com/CVHub520/X-AnyLabeling"
+__url__ = "https://github.com/CVHub520/X-AnyLabeling"

anylabeling/checks.py

@@ -8,7 +8,7 @@
     indent_text,
 )
 
-logging.basicConfig(level=logging.INFO, format='%(message)s')
+logging.basicConfig(level=logging.INFO, format="%(message)s")
 logger = logging.getLogger(__name__)
 
 if __name__ == "__main__":
@@ -19,11 +19,11 @@
     }
     system_info, pkg_info = collect_system_info()
 
-    logger.info(format_color(format_bold("Application Information:"), '36'))
+    logger.info(format_color(format_bold("Application Information:"), "36"))
     logger.info(indent_text(pprint.pformat(app_info)))
 
-    logger.info(format_color(format_bold("\nSystem Information:"), '36'))
+    logger.info(format_color(format_bold("\nSystem Information:"), "36"))
     logger.info(indent_text(pprint.pformat(system_info)))
 
-    logger.info(format_color(format_bold("\nPackage Information:"), '36'))
-    logger.info(indent_text(pprint.pformat(pkg_info)))
+    logger.info(format_color(format_bold("\nPackage Information:"), "36"))
+    logger.info(indent_text(pprint.pformat(pkg_info)))

anylabeling/config.py

@@ -14,6 +14,7 @@
 
 current_config_file = None
 
+
 def update_dict(target_dict, new_dict, validate_item=None):
     for key, value in new_dict.items():
         if validate_item:
@@ -68,7 +69,9 @@ def validate_config_item(key, value):
         )
 
 
-def get_config(config_file_or_yaml=None, config_from_args=None, show_msg=False):
+def get_config(
+    config_file_or_yaml=None, config_from_args=None, show_msg=False
+):
     # 1. Load default configuration
     config = get_default_config()
 
@@ -82,12 +85,18 @@ def get_config(config_file_or_yaml=None, config_from_args=None, show_msg=False):
             config_from_yaml = yaml.safe_load(f)
     update_dict(config, config_from_yaml, validate_item=validate_config_item)
     if show_msg:
-        logger.info(f"🔧️ Initializing config from local file: {config_file_or_yaml}")
+        logger.info(
+            f"🔧️ Initializing config from local file: {config_file_or_yaml}"
+        )
 
     # 3. Update configuration with command line arguments
     if config_from_args:
-        update_dict(config, config_from_args, validate_item=validate_config_item)
+        update_dict(
+            config, config_from_args, validate_item=validate_config_item
+        )
         if show_msg:
-            logger.info(f"🔄 Updated config from CLI arguments: {config_from_args}")
+            logger.info(
+                f"🔄 Updated config from CLI arguments: {config_from_args}"
+            )
 
     return config

anylabeling/resources/resources.py

@@ -18226,10 +18226,13 @@
     rcc_version = 2
     qt_resource_struct = qt_resource_struct_v2
 
+
 def qInitResources():
     QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
 
+
 def qCleanupResources():
     QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
 
+
 qInitResources()

anylabeling/services/auto_labeling/__base__/rtmdet.py

@@ -8,7 +8,7 @@
 
 """
 The onnxruntime demo of the RTMDet model
-Ref: 
+Ref:
     https://github.com/open-mmlab/mmyolo/tree/dev/configs/rtmdet
     https://platform.openmmlab.com/deploee/onnx-detail?search=rtmdet&tag=&page=1
     https://github.com/Tau-J/rtmlib/blob/main/rtmlib/tools/object_detection/rtmdet.py
@@ -122,50 +122,9 @@ def postprocess(
 
         if outputs.shape[-1] == 4:
             # onnx without nms module
-
-            grids = []
-            expanded_strides = []
-            strides = [8, 16, 32]
-
-            hsizes = [self.model_input_size[0] // stride for stride in strides]
-            wsizes = [self.model_input_size[1] // stride for stride in strides]
-
-            for hsize, wsize, stride in zip(hsizes, wsizes, strides):
-                xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-                grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-                grids.append(grid)
-                shape = grid.shape[:2]
-                expanded_strides.append(np.full((*shape, 1), stride))
-
-            grids = np.concatenate(grids, 1)
-            expanded_strides = np.concatenate(expanded_strides, 1)
-            outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
-            outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
-
-            predictions = outputs[0]
-            boxes = predictions[:, :4]
-            scores = predictions[:, 4:5] * predictions[:, 5:]
-
-            boxes_xyxy = np.ones_like(boxes)
-            boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-            boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-            boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-            boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-            boxes_xyxy /= ratio
-            dets = multiclass_nms(
-                boxes_xyxy,
-                scores,
-                nms_thr=self.nms_thr,
-                score_thr=self.score_thr,
+            raise NotImplementedError(
+                "RTMDet without nms module is not supported yet."
             )
-            if dets is not None:
-                pack_dets = (dets[:, :4], dets[:, 4], dets[:, 5])
-                final_boxes, final_scores, final_cls_inds = pack_dets
-                isscore = final_scores > 0.3
-                iscat = final_cls_inds == 0
-                isbbox = [i and j for (i, j) in zip(isscore, iscat)]
-                final_boxes = final_boxes[isbbox]
-
         elif outputs.shape[-1] == 5:
             # onnx contains nms module
             pack_dets = (outputs[0, :, :4], outputs[0, :, 4])

anylabeling/services/auto_labeling/__base__/sam2.py

@@ -84,7 +84,9 @@ def __init__(self, path: str, device: str) -> None:
             providers = ["CUDAExecutionProvider"]
         sess_options = ort.SessionOptions()
         sess_options.log_severity_level = 3
-        self.session = ort.InferenceSession(path, providers=providers, sess_options=sess_options)
+        self.session = ort.InferenceSession(
+            path, providers=providers, sess_options=sess_options
+        )
 
         # Get model info
         self.get_input_details()
@@ -158,10 +160,12 @@ def __init__(
         # Initialize model
         providers = ["CPUExecutionProvider"]
         if device.lower() == "gpu":
-            providers = ["CUDAExecutionProvider"]   
+            providers = ["CUDAExecutionProvider"]
         sess_options = ort.SessionOptions()
         sess_options.log_severity_level = 3
-        self.session = ort.InferenceSession(path, providers=providers, sess_options=sess_options)
+        self.session = ort.InferenceSession(
+            path, providers=providers, sess_options=sess_options
+        )
 
         self.orig_im_size = (
             orig_im_size if orig_im_size is not None else encoder_input_size

anylabeling/services/auto_labeling/__base__/yolo.py

@@ -406,12 +406,16 @@ def predict_shapes(self, image, image_path=None):
             zip(boxes, class_ids, scores, points, keypoints, track_ids)
         ):
             if self.task == "det" or self.show_boxes:
-                shape = self.create_rectangle_shape(box, score, i, class_id, track_id)
+                shape = self.create_rectangle_shape(
+                    box, score, i, class_id, track_id
+                )
                 shapes.append(shape)
             if self.task == "seg":
                 if len(point) < 3:
                     continue
-                shape = self.create_polygon_shape(point, score, class_id, track_id)
+                shape = self.create_polygon_shape(
+                    point, score, class_id, track_id
+                )
                 shapes.append(shape)
             if self.task == "pose":
                 label = str(self.classes[int(class_id)])
@@ -428,7 +432,9 @@ def predict_shapes(self, image, image_path=None):
                         or s < self.kpt_thres
                     ):
                         continue
-                    shape = self.create_keypoint_shape((x, y), keypoint_name, s, j, i, track_id)
+                    shape = self.create_keypoint_shape(
+                        (x, y), keypoint_name, s, j, i, track_id
+                    )
                     shapes.append(shape)
             if self.task == "obb":
                 shape = self.create_obb_shape(box, score, class_id, track_id)

anylabeling/services/auto_labeling/efficientvit_sam.py

@@ -421,7 +421,6 @@ def predict_shapes(self, image, filename=None) -> AutoLabelingResult:
                 image_embedding = cached_data
             else:
                 cv_image = qt_img_to_rgb_cv_img(image, filename)
-                origin_image_size = cv_image.shape[:2]
                 if self.stop_inference:
                     return AutoLabelingResult([], replace=False)
                 image_embedding = self.encoder_model(cv_image)

anylabeling/services/auto_labeling/engines/build_onnx_engine.py

@@ -4,7 +4,9 @@
 
 
 class OnnxBaseModel:
-    def __init__(self, model_path, device_type: str = "cpu", log_severity_level: int = 0):
+    def __init__(
+        self, model_path, device_type: str = "cpu", log_severity_level: int = 3
+    ):
         self.sess_opts = ort.SessionOptions()
         self.sess_opts.log_severity_level = log_severity_level
         if "OMP_NUM_THREADS" in os.environ:

anylabeling/services/auto_labeling/grounding_sam2.py

@@ -94,7 +94,7 @@ def __init__(self, model_config, on_message) -> None:
             raise FileNotFoundError(
                 QCoreApplication.translate(
                     "Model",
-                    f"Could not download or initialize encoder of SAM2.",
+                    "Could not download or initialize encoder of SAM2.",
                 )
             )
         decoder_model_abs_path = self.get_model_abs_path(

anylabeling/services/auto_labeling/internimage_cls.py

@@ -39,7 +39,7 @@ def __init__(self, model_config, on_message) -> None:
             raise FileNotFoundError(
                 QCoreApplication.translate(
                     "Model",
-                    f"Could not download or initialize InternImage model.",
+                    "Could not download or initialize InternImage model.",
                 )
             )
         self.net = OnnxBaseModel(model_abs_path, __preferred_device__)

anylabeling/services/auto_labeling/model.py

@@ -178,9 +178,7 @@ def get_filename_from_url(url):
             ellipsis_download_url = (
                 download_url[:20] + "..." + download_url[-20:]
             )
-        logger.info(
-            f"Downloading {ellipsis_download_url} to {model_abs_path}"
-        )
+        logger.info(f"Downloading {ellipsis_download_url} to {model_abs_path}")
         try:
             # Download and show progress
             def _progress(count, block_size, total_size):