Code Refactor ruff check --fix --extend-select I (#47)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

.github/workflows/format.yml

@@ -6,9 +6,9 @@ name: Ultralytics Actions
 
 on:
   push:
-    branches: [main,master]
+    branches: [main]
   pull_request:
-    branches: [main,master]
+    branches: [main]
 
 jobs:
   format:

README.md

@@ -57,7 +57,7 @@ The repository contains various methods for vehicle speed estimation. If you're
 
 <!-- Sample results image -->
 
-<img src="https://github.com/ultralytics/velocity/blob/main/results.jpg"> 
+<img src="https://github.com/ultralytics/velocity/blob/main/results.jpg">
 
 # 📚 Citation
 

utils/NLS.py

@@ -202,7 +202,7 @@ def fcnNLS_batch(K, P, pw, cw):  # solves for pxyz, cxyz[1:], crpy[1:]
     z[nanz] = 0
     crpy = np.zeros((nc, 3))
     x = np.concatenate((pw, cw[1:], crpy)).ravel()  # [tp_pos, cam_pos, cam_rpy, K3]
-    range_cal = norm(cw[1])
+    norm(cw[1])
 
     def fzKautograd_batch(x, K, nc, nt):  # for autograd
         pw = x[: nt * 3].reshape(nt, 3)
@@ -247,7 +247,7 @@ def fzKautograd_batch(x, K, nc, nt):  # for autograd
     pw = x[:j].reshape(nt, 3)
     cw = x[j : j + nc * 3].reshape(nc, 3)  # cam pos
     cw = np.concatenate((np.zeros((1, 3)), cw), 0)
-    ca = x[j + nc * 3 : j + nc * 3 * 2].reshape(nc, 3)  # cam rpy
+    x[j + nc * 3 : j + nc * 3 * 2].reshape(nc, 3)  # cam rpy
     return cw, pw
 
 
@@ -325,5 +325,5 @@ def fzKautograd_batch(x, K, nc, nt):  # for autograd
     pw = x[:j].reshape(nt, 3)
     cw = sc2cc(sc) @ C  # cam pos
     cw = np.concatenate((np.zeros((1, 3)), cw), 0)
-    ca = x[j : j + 3]  # cam rpy
+    x[j : j + 3]  # cam rpy
     return cw, pw

vidExample.py

@@ -151,7 +151,6 @@ def vidExamplefcn():
         P[0:2, vg, i] = p.T  # xy
         P[2:4, vp, i] = p_.T  # xy_proj
         P[4, vg, i] = i
-        im0 = im
 
         msvFrame = 5
         if i == msvFrame:
@@ -168,7 +167,7 @@ def vidExamplefcn():
         # imrgb = cv2.cvtColor(imbgr,cv2.COLOR_BGR2RGB)
         # plots.imshow(cv2.cvtColor(imrgb,cv2.COLOR_BGR2HSV_FULL)[:,:,0])
         im_gaussian = cv2.GaussianBlur(im, (3, 3), 0)
-        im_canny = cv2.Canny(im_gaussian, 100, 200)
+        cv2.Canny(im_gaussian, 100, 200)
         # plots.imshow(cv2.GaussianBlur(im_canny, (9, 9), 0))
 
     if isVideo: