fix: notebook errata pass -- chap2 through app.ipynb all clean

Makefile

@@ -4,11 +4,19 @@ BLUE=\033[0;34m
 BLACK=\033[0;30m
 
 help:
-	@echo "$(BLUE) make dist - build dist files"
+	@echo "$(BLUE) make test - run the fast test suite"
+	@echo " make testall - run the full test suite, including slow notebook/example tests"
+	@echo " make dist - build dist files"
 	@echo " make upload - upload to PyPI"
 	@echo " make clean - remove dist and docs build files"
 	@echo " make help - this message$(BLACK)"
 
+test: .FORCE
+	pytest
+
+testall: .FORCE
+	pytest --runall
+
 dist: .FORCE
 	# $(MAKE) test
 	python -m build

RVC3/examples/imu_data.py

@@ -20,7 +20,7 @@
 
 import matplotlib.pyplot as plt
 
-def tumble():
+def IMU():
     # accelerometer
     g0 = unitvec( [0, 0, 9.8] ).T
     gbias = 0.02 * np.r_[2, -2, 2].T  # bias 2% of norm
@@ -30,7 +30,7 @@ def tumble():
     mbias = 0.02 * np.r_[-1, -1, 2]   # bias 2# of norm
 
     # gyro
-    wbias = 0.05 * np.r_[-1, 2, -1] # bias 5% of max
+    wbias = 0.05 * np.r_[-1, 2, -1] # bias 10% of max
 
     ## simulation
 
@@ -56,7 +56,7 @@ def tumble():
         w0, t)
 
     # Solve for simulated sensor readings and true attitude
-    # 1 column per timestep
+    # 1 row per timestep
     am = np.zeros(omega.shape)
     mm = np.zeros(omega.shape)
 
@@ -71,8 +71,9 @@ def tumble():
     # add bias to measured
     wm = omega + wbias
 
-    data = namedtuple('tumble', 't omega_true attitude_true g B gyro accel magno')
-    return data(t, omega, truth, g0, m0, wm, am, mm)
+    imu = namedtuple('imu', 't dt gyro accel magno')
+    true = namedtuple('true', 't dt omega orientation g B')
+    return true(t, dt, omega, truth, g0, m0), imu(t, dt, wm, am, mm)
 
 if __name__ == "__main__":
 
@@ -82,14 +83,14 @@ def plot(t, y, title):
         plt.grid(True)
         plt.title(title)
 
-    data = tumble()
+    true, imu = IMU()
 
-    print(data.attitude_true[100])
-    print(data.attitude_true[100].rpy())
+    print(true.orientation[100])
+    print(true.orientation[100].rpy())
 
-    plot(data.t, data.attitude_true.rpy(), 'attitude')
-    plot(data.t, data.gyro, 'gyro')
-    plot(data.t, data.accel, 'accel')
-    plot(data.t, data.magno, 'magno')
+    plot(true.t, true.orientation.rpy(), 'attitude')
+    plot(imu.t, imu.gyro, 'gyro')
+    plot(imu.t, imu.accel, 'accel')
+    plot(imu.t, imu.magno, 'magno')
 
     plt.show(block=True)

RVC3/examples/visodom.py

@@ -18,11 +18,15 @@
 #!/usr/bin/env python3
 
 # from RVC3.tools import rvcprint
+import sys
 import numpy as np
 import matplotlib.pyplot as plt
 from machinevisiontoolbox import *
 import spatialmath.base as smb
 
+# optional CLI arg: max number of frames to process, eg. `%run -m visodom 50`
+max_frames = int(sys.argv[1]) if len(sys.argv) > 1 else None
+
 # load .enpeda dataset, 12bit pixel values
 args = dict(mono=True, dtype="uint8", maxintval=4095, roi=[20, 750, 20, 480])
 try:
@@ -54,16 +58,18 @@
 n_no_overlap = 0
 n_optimized = 0
 
-for left, right in zip(lefts, rights):
+for left_img, right_img in zip(lefts, rights):
+    if max_frames is not None and nframes >= max_frames:
+        break
     nframes += 1
-    print("-----------------", left.id)
+    print("-----------------", left_img.id)
     # plt.clf()
     # plt.imshow(image.A, cmap='gray')
     # smb.plot_text((20, 420), f"frame {image.id}", color='w', backgroundcolor='k', fontsize=12)
 
     # find corner features
-    orbL = left.ORB(nfeatures=400, id="index")
-    orbR = right.ORB(nfeatures=400)
+    orbL = left_img.ORB(nfeatures=400, id="index")
+    orbR = right_img.ORB(nfeatures=400)
 
     # robustly match left and right corner features
     # - stereo match
@@ -74,7 +80,7 @@
         # too few/degenerate stereo correspondences to triangulate this
         # frame at all -- skip it entirely, keep the last good frame as
         # the temporal reference for the next one
-        print(f"  stereo F estimation failed for frame {left.id}: {e}")
+        print(f"  stereo F estimation failed for frame {left_img.id}: {e}")
         n_lr_fail += 1
         continue
     print(matchLR)
@@ -91,34 +97,36 @@
     P, d = lines1.closest_to_line(lines2)
     print(np.nanmedian(d))
 
-    if left.id > 0:
+    if left_img.id > 0:
         # if we have a previous frame
 
         # display two sequential stereo pairs
         plt.clf()
-        view4 = Image.Tile([left, right, left_prev, right_prev], columns=2, sep=0)
+        view4 = Image.Tile(
+            [left_img, right_img, left_prev, right_prev], columns=2, sep=0
+        )
         plt.imshow(view4.A, cmap="gray")
 
-        matchLR.plot_correspondence("y", offset=(left.width, 0), linewidth=0.5)
+        matchLR.plot_correspondence("y", offset=(left_img.width, 0), linewidth=0.5)
 
         # temporal matching
         matchFB = orbL.match(orbL_prev)
         try:
             F = matchFB.estimate(cam.points2F, method="ransac")
             print(matchFB)
             matchFB = matchFB.inliers  # keep the inliers
-            matchFB.plot_correspondence("y", offset=(0, left.height), linewidth=0.5)
+            matchFB.plot_correspondence("y", offset=(0, left_img.height), linewidth=0.5)
             plt.pause(0.1)
         except ValueError as e:
             # too few/degenerate temporal correspondences -- same downstream
             # effect as zero overlapping landmarks below (no valid frame
             # motion estimate), but caught earlier since points2F() itself
             # can't even find a fundamental matrix here
-            print(f"  temporal F estimation failed for frame {left.id}: {e}")
+            print(f"  temporal F estimation failed for frame {left_img.id}: {e}")
             n_fb_fail += 1
             matchFB = None
 
-        # if left.id == 10:
+        # if left_img.id == 10:
         #     rvcprint.rvcprint(thicken=None)
 
         # now create a bundle adjustment problem, if we have a usable
@@ -134,9 +142,7 @@
             c_left = ba.add_view(
                 SE3(), fixed=True
             )  # first camera at origin (current frame)
-            c_leftprev = ba.add_view(
-                SE3()
-            )  # initial guess, zero motion (prev frame)
+            c_leftprev = ba.add_view(SE3())  # initial guess, zero motion (prev frame)
 
             for k, Pk in enumerate(P.T):  # for every 3D point from stereo
                 if np.any(np.isnan(Pk)):
@@ -148,9 +154,7 @@
                     continue
                 landmark = ba.add_landmark(Pk)
                 ba.add_projection(c_left, landmark, m.p1)  # current left camera
-                ba.add_projection(
-                    c_leftprev, landmark, m.p2
-                )  # previous left camera
+                ba.add_projection(c_leftprev, landmark, m.p2)  # previous left camera
                 landmarks_added = True
 
         if landmarks_added:
@@ -161,16 +165,16 @@
         else:
             print(
                 f"  no overlapping stereo/temporal landmarks for frame "
-                f"{left.id} -- skipping bundle adjustment"
+                f"{left_img.id} -- skipping bundle adjustment"
             )
             n_no_overlap += 1
             displacements.append(np.full(6, np.nan))
             errors.append(np.nan)
 
     # keep images and features for next cycle
     orbL_prev = orbL
-    left_prev = left
-    right_prev = right
+    left_prev = left_img
+    right_prev = right_img
 
 print()
 print("===== summary =====")

RVC3/models/IBVS-holonomic.bd

@@ -613,7 +613,7 @@
                     false
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [

RVC3/models/IBVS-nonholonomic.bd

@@ -299,11 +299,11 @@
                     false
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [
-                    "kwargs",
+                    "fkwargs",
                     {}
                 ],
                 [
@@ -491,11 +491,11 @@
                     false
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [
-                    "kwargs",
+                    "fkwargs",
                     {}
                 ],
                 [
@@ -572,11 +572,11 @@
                     true
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [
-                    "kwargs",
+                    "fkwargs",
                     {}
                 ],
                 [
@@ -996,7 +996,7 @@
                     false
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [
@@ -1162,7 +1162,7 @@
                     false
                 ],
                 [
-                    "args",
+                    "fargs",
                     []
                 ],
                 [

RVC3/models/IBVS-quadrotor.bd

@@ -513,11 +513,11 @@
                     "=quadrotor"
                 ],
                 [
-                    "maxw",
+                    "wmax",
                     1000
                 ],
                 [
-                    "minw",
+                    "wmin",
                     5
                 ],
                 [

RVC3/models/braitenberg.py

@@ -52,7 +52,7 @@ def sensorfunc(x, offset):
     scale=[0, 100],
     size=5,
     shape="box",
-    trail=True,
+    path="b:",
     name="sensor field",
     init=background_graphics,
 )

RVC3/models/feedforward-main.py

@@ -28,5 +28,5 @@
 
 import matplotlib.pyplot as plt
 
-plt.plot(out.t, out.x[:, 1], out.t, out.y0[:, 1])
+plt.plot(out.t, out.x[:, 1], out.t, out.y[:, 1])
 plt.show(block=True)