Add evaluation and visualization notebooks

Author: sarlinpe

notebooks/generate_proto_py.sh

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+MAPLAB="../catkin_dependencies/maplab"
+MAPLAB_COMMON="$MAPLAB/common/maplab-common/proto"
+
+PROTO="../deep-relocalization/proto/deep-relocalization"
+protoc -I=$MAPLAB_COMMON:$PROTO --python_out=./ \
+    $PROTO/descriptor_index.proto \
+    $MAPLAB_COMMON/maplab-common/id.proto \
+    $MAPLAB_COMMON/maplab-common/eigen.proto

notebooks/nclt_evaluation.ipynb

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+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+
+
+def plot_imgs(imgs, titles=None, cmap='brg', ylabel='', normalize=True, ax=None,
+              r=(0, 1), dpi=100):
+    n = len(imgs)
+    if not isinstance(cmap, list):
+        cmap = [cmap]*n
+    if ax is None:
+        _, ax = plt.subplots(1, n, figsize=(6*n, 6), dpi=dpi)
+        if n == 1:
+            ax = [ax]
+    else:
+        if not isinstance(ax, list):
+            ax = [ax]
+        assert len(ax) == len(imgs)
+    for i in range(n):
+        if len(imgs[i].shape) == 3:
+            if imgs[i].shape[-1] == 3:
+                imgs[i] = imgs[i][..., ::-1]  # BGR to RGB
+            elif imgs[i].shape[-1] == 1:
+                imgs[i] = imgs[i][..., 0]
+        if len(imgs[i].shape) == 2 and cmap[i] == 'brg':
+            cmap[i] = 'gray'
+        ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmap[i]),
+                     vmin=None if normalize else r[0],
+                     vmax=None if normalize else r[1])
+        if titles:
+            ax[i].set_title(titles[i])
+        ax[i].get_yaxis().set_ticks([])
+        ax[i].get_xaxis().set_ticks([])
+        for spine in ax[i].spines.values():  # remove frame
+            spine.set_visible(False)
+    ax[0].set_ylabel(ylabel)
+    plt.tight_layout()
+
+
+def draw_datches(img1, kp1, img2, kp2, matches, color=None, kp_radius=5,
+                 thickness=2, margin=20):
+    # Create frame
+    if len(img1.shape) == 3:
+        new_shape = (max(img1.shape[0], img2.shape[0]),
+                     img1.shape[1]+img2.shape[1]+margin,
+                     img1.shape[2])
+    elif len(img1.shape) == 2:
+        new_shape = (max(img1.shape[0],
+                     img2.shape[0]),
+                     img1.shape[1]+img2.shape[1]+margin)
+    new_img = np.ones(new_shape, type(img1.flat[0]))*255
+
+    # Place original images
+    new_img[0:img1.shape[0], 0:img1.shape[1]] = img1
+    new_img[0:img2.shape[0],
+            img1.shape[1]+margin:img1.shape[1]+img2.shape[1]+margin] = img2
+
+    # Draw lines between matches
+    if color:
+        c = color
+    for m in matches:
+        # Generate random color for RGB/BGR and grayscale images as needed.
+        if not color:
+            if len(img1.shape) == 3:
+                c = np.random.randint(0, 256, 3)
+            else:
+                c = np.random.randint(0, 256)
+            c = (int(c[0]), int(c[1]), int(c[2]))
+
+        end1 = tuple(np.round(kp1[m.trainIdx].pt).astype(int))
+        end2 = tuple(np.round(kp2[m.queryIdx].pt).astype(int)
+                     + np.array([img1.shape[1]+margin, 0]))
+        cv2.line(new_img, end1, end2, c, thickness, lineType=cv2.LINE_AA)
+        cv2.circle(new_img, end1, kp_radius, c, thickness, lineType=cv2.LINE_AA)
+        cv2.circle(new_img, end2, kp_radius, c, thickness, lineType=cv2.LINE_AA)
+    return new_img