Revert "much stricter ruff linting"

This reverts commit 310dbf1574.

modules/control/proc/openpose/__init__.py

@@ -194,14 +194,14 @@ class OpenposeDetector:
     def __call__(self, input_image, detect_resolution=512, image_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", **kwargs):
         self.to(devices.device)
         if hand_and_face is not None:
-            warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning, stacklevel=2)
+            warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning)
             include_hand = hand_and_face
             include_face = hand_and_face
         if "return_pil" in kwargs:
-            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning, stacklevel=2)
+            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
             output_type = "pil" if kwargs["return_pil"] else "np"
         if type(output_type) is bool:
-            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions", stacklevel=2)
+            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
             if output_type:
                 output_type = "pil"
         if not isinstance(input_image, np.ndarray):

modules/control/proc/openpose/face.py

@@ -328,7 +328,7 @@ class Face(object):
 
     def __call__(self, face_img):
         device = next(iter(self.model.parameters())).device
-        H, W, _C = face_img.shape
+        H, W, C = face_img.shape
 
         w_size = 384
         x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5

modules/control/proc/openpose/hand.py

@@ -32,7 +32,7 @@ class Hand(object):
         wsize = 128
         heatmap_avg = np.zeros((wsize, wsize, 22))
 
-        Hr, Wr, _Cr = oriImgRaw.shape
+        Hr, Wr, Cr = oriImgRaw.shape
 
         oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)