Copied initial tools.

scripts/draw_utils.py

@@ -0,0 +1,46 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from skelda import utils_view
+
+# ==================================================================================================
+
+
+def show_poses2d(bodies, images, joint_names, title=""):
+    num_imgs = len(images)
+    rowbreak = int(num_imgs / 2.0 + 0.5)
+    fig, axs = plt.subplots(2, rowbreak, figsize=(30, 20))
+    fig.suptitle(title, fontsize=20)
+
+    if isinstance(bodies, np.ndarray):
+        bodies = bodies.tolist()
+
+    # Draw skeletons into images
+    for i, image in enumerate(images):
+        colors = plt.cm.hsv(np.linspace(0, 1, len(bodies[i]), endpoint=False)).tolist()
+        colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
+
+        for j, body in enumerate(bodies[i]):
+            image = utils_view.draw_body_in_image(image, body, joint_names, colors[j])
+
+    # Rescale image range for plotting
+    images = [img / 255.0 for img in images]
+
+    if rowbreak == 1:
+        axs[0].imshow(images[0])
+        if len(images) == 2:
+            axs[1].imshow(images[1])
+        else:
+            # Optionally delete last empty plot
+            fig.delaxes(axs[1])
+
+    else:
+        for i in range(rowbreak):
+            axs[0][i].imshow(images[i])
+            if i + rowbreak < num_imgs:
+                axs[1][i].imshow(images[i + rowbreak])
+            else:
+                # Optionally delete last empty plot
+                fig.delaxes(axs[1][i])
+
+    return fig

scripts/utils_2d_pose.py

@@ -0,0 +1,71 @@
+import os
+
+import numpy as np
+from mmpose.apis import MMPoseInferencer
+
+# ==================================================================================================
+
+filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
+
+# ==================================================================================================
+
+
+def load_model():
+    print("Loading mmpose model ...")
+
+    model = MMPoseInferencer(
+        pose2d="/mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py",
+        pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-384x288-3f5a1437_20230504.pth",
+        det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
+        det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
+        det_cat_ids=[0],
+    )
+
+    print("Loaded mmpose model")
+    return model
+
+
+def load_wb_model():
+    print("Loading mmpose whole body model ...")
+
+    model = MMPoseInferencer(
+        pose2d="/mmpose/projects/rtmpose/rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py",
+        pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth",
+        det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
+        det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
+        det_cat_ids=[0],
+    )
+
+    print("Loaded mmpose model")
+    return model
+
+
+# ==================================================================================================
+
+
+def get_2d_pose(model, imgs, num_joints=17):
+    """See: https://mmpose.readthedocs.io/en/latest/user_guides/inference.html#basic-usage"""
+
+    result_generator = model(imgs, show=False)
+    new_poses = []
+    for _ in range(len(imgs)):
+        result = next(result_generator)
+
+        poses = []
+        for i in range(len(result["predictions"][0])):
+            kpts = result["predictions"][0][i]["keypoints"]
+            scores = result["predictions"][0][i]["keypoint_scores"]
+
+            kpts = np.array(kpts)
+            scores = np.array(scores).reshape(-1, 1)
+            scores = np.clip(scores, 0, 1)
+            pose = np.concatenate((kpts, scores), axis=-1)
+            poses.append(pose)
+
+        if len(poses) == 0:
+            poses.append(np.zeros([num_joints, 3]))
+
+        poses = np.array(poses)
+        new_poses.append(poses)
+
+    return new_poses