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plots.py

@@ -85,7 +85,7 @@ def plotresults(cam, im, P, S, B, bbox):
         plot_height=300,
         x_axis_label="image",
         y_axis_label="distance (m)",
-        title="Distance = %.2fm in %.3fs" % (S[-1, 7], S[-1, 5] - S[0, 5]),
+        title=f"Distance = {S[-1, 7]:.2f}m in {S[-1, 5] - S[0, 5]:.3f}s",
         tools="save,reset,hover",
         active_inspect="hover",
     )
@@ -98,7 +98,7 @@ def plotresults(cam, im, P, S, B, bbox):
         plot_height=300,
         x_axis_label="image",
         y_axis_label="speed (km/h)",
-        title="Speed = %.2f +/- %.2f km/h" % (S[1:, 8].mean(), S[1:, 8].std()),
+        title=f"Speed = {S[1:, 8].mean():.2f} +/- {S[1:, 8].std():.2f} km/h",
         tools="save,reset,hover",
         active_inspect="hover",
     )

utils/MSV.py

@@ -84,7 +84,7 @@ def fcnMSV2_t(K, P, B, vg, i):  # solves for 1 camera translation
         JT = (JT - zhat) / dx
         JTJ = JT @ JT.T  # J.T @ J
         delta = np.linalg.inv(JTJ + mdm) @ JT @ residual * min(((i + 1) * 0.01) ** 2, 1)
-        print("%g: f=%g, x=%s" % (i, rms(z - zhat), rms(delta)))
+        print(f"{i:g}: f={rms(z - zhat):g}, x={rms(delta)}")
         x = x + delta
         if rms(delta) < 1e-8:
             break
@@ -276,7 +276,7 @@ def grad_func(x, u0, U, K, z):  # calculates the gradient
         v_cap = v / (1 - (beta_2**i))  # calculates the bias-corrected estimates
         delta = (alpha * m_cap) / (v_cap**0.5 + epsilon)
         x = x - delta  # updates the parameters
-        print("Residual %g,    Params: %s" % (r[i], x[:]))
+        print(f"Residual {r[i]:g},    Params: {x[:]}")
         xi[i] = x
         if rms(delta) < 1e-5:  # convergence check
             break

utils/NLS.py

@@ -234,12 +234,12 @@ def fzKautograd_batch(x, K, nc, nt):  # for autograd
         delta = np.linalg.inv(JT @ JT.T + mdm) @ JT @ (z - zhat) * 0.9
         x = x + delta
         # x[nt * 3:nt * 3 + nc * 3] *= range_cal / norm(x[nt * 3:nt * 3 + 3])  # calibrate scale
-        print("%g: %.3fs, f=%g, x=%s" % (i, time.time() - tic, rms(z - zhat), rms(delta)))
+        print(f"{i:g}: {time.time() - tic:.3f}s, f={rms(z - zhat):g}, x={rms(delta)}")
         if rms(delta) < 1e-7:
             break
     else:
         print("WARNING: fcnNLS_batch() reaching max iterations!")
-    print("fcnNLS_batch done in %g steps, %.3fs, f=%g" % (i, time.time() - tic, rms(z - zhat)))
+    print(f"fcnNLS_batch done in {i:g} steps, {time.time() - tic:.3f}s, f={rms(z - zhat):g}")
 
     j = nt * 3
     pw = x[:j].reshape(nt, 3)
@@ -313,7 +313,7 @@ def fzKautograd_batch(x, K, nc, nt):  # for autograd
             break
     else:
         print("WARNING: fcnNLS_batch() reaching max iterations!")
-    print("fcnNLS_batch2 done in %g steps, %.3fs, f=%g" % (i, time.time() - tic, rms(z - zhat)))
+    print(f"fcnNLS_batch2 done in {i:g} steps, {time.time() - tic:.3f}s, f={rms(z - zhat):g}")
 
     j = nt * 3
     sc = np.zeros((nc, 3))

utils/vid2images.py

@@ -18,7 +18,7 @@
     cap.set(1, i)
     success, im = cap.read()  # read frame
     if success:
-        print("image %g/%g ..." % (cap.get(cv2.CAP_PROP_POS_FRAMES), cap.get(cv2.CAP_PROP_FRAME_COUNT)))
+        print(f"image {cap.get(cv2.CAP_PROP_POS_FRAMES):g}/{cap.get(cv2.CAP_PROP_FRAME_COUNT):g} ...")
         cv2.imwrite(newdir + str(i) + ".jpg", im)
     else:
         cap.release()

vidExample.py

@@ -162,7 +162,7 @@ def vidExamplefcn():
         # Print image[i] results
         proc_dt[i] = time.time() - tic
         S[i, :] = (i, proc_dt[i], vg.sum(), residuals, dt, B[i, 12] - t0, dr, r, dr / dt * 3.6)
-        print("%13g%13.3f%13g%13.3f%13.3f%13.3f%13.2f%13.2f%13.1f" % tuple(S[i, :]))
+        print("{:13g}{:13.3f}{:13g}{:13.3f}{:13.3f}{:13.3f}{:13.2f}{:13.2f}{:13.1f}".format(*tuple(S[i, :])))
 
         # imrgb = cv2.cvtColor(imbgr,cv2.COLOR_BGR2RGB)
         # plots.imshow(cv2.cvtColor(imrgb,cv2.COLOR_BGR2HSV_FULL)[:,:,0])
@@ -174,8 +174,8 @@ def vidExamplefcn():
         cap.release()  # Release the video capture object
 
     dta = time.time() - cput0
-    print("\nSpeed = %.2f +/- %.2f km/h\nRes = %.3f pixels" % (S[1:, 8].mean(), S[1:, 8].std(), S[1:, 3].mean()))
-    print("Processed %g images: %s in %.2fs (%.2ffps)\n" % (n, frames[:], dta, n / dta))
+    print(f"\nSpeed = {S[1:, 8].mean():.2f} +/- {S[1:, 8].std():.2f} km/h\nRes = {S[1:, 3].mean():.3f} pixels")
+    print(f"Processed {n:g} images: {frames[:]} in {dta:.2f}s ({n / dta:.2f}fps)\n")
 
     plots.plotresults(cam, im // 2 + imfirst // 2, P, S, B, bbox=boxb)  # // is integer division