bpycv: computer vision and deep learning utils for Blender

Contents: Features | Install | Fast Demo | Tips

Render instance annoatation, RGB image and depth in one line code

▮ Features

• Render depth
• Render annotations for semantic segmentation, instance segmentation, and panoptic segmentation
• Generate 6DoF pose ground truth
• Pre-define domain randomization: light
• Pre-define domain randomization: background
• Pre-define domain randomization: distractors
• Pre-define domain randomization: textures
• Support docker: docker run -v /tmp:/tmp diyer22/bpycv
• To Cityscapes format
• To COCO format

News: We win 🥈2nd place in IROS 2020 Open Cloud Robot Table Organization Challenge (OCRTOC)

▮ Install

bpycv support Blender 2.8, 2.9

Example for Blender 2.92:

cd <path to blender>/2.92/python/bin
./python3.7m -m ensurepip  # get pip
./python3.7m -m pip install -U pip setuptools wheel 
./python3.7m -m pip install -U bpycv

▮ Fast Demo

1. Instance Segmentation and Depth Demo

Copy-paste this code to Scripting/Text Editor and click Run Script button(or Alt+P)

import cv2
import bpy
import bpycv
import random
import numpy as np

# remove all MESH objects
[bpy.data.objects.remove(obj) for obj in bpy.data.objects if obj.type == "MESH"]

for index in range(1, 20):
    # create cube and sphere as instance at random location
    location = [random.uniform(-2, 2) for _ in range(3)]
    if index % 2:
        bpy.ops.mesh.primitive_cube_add(size=0.5, location=location)
        categories_id = 1
    else:
        bpy.ops.mesh.primitive_uv_sphere_add(radius=0.5, location=location)
        categories_id = 2
    obj = bpy.context.active_object
    # set each instance a unique inst_id, which is used to generate instance annotation.
    obj["inst_id"] = categories_id * 1000 + index

# render image, instance annoatation and depth in one line code
# result["ycb_meta"] is 6d pose GT
result = bpycv.render_data()

# save result
cv2.imwrite(
    "demo-rgb.jpg", result["image"][..., ::-1]
)  # transfer RGB image to opencv's BGR

# save instance map as 16 bit png
# the value of each pixel represents the inst_id of the object to which the pixel belongs
cv2.imwrite("demo-inst.png", np.uint16(result["inst"]))

# convert depth units from meters to millimeters
depth_in_mm = result["depth"] * 1000
cv2.imwrite("demo-depth.png", np.uint16(depth_in_mm))  # save as 16bit png

# visualization inst_rgb_depth for human
cv2.imwrite("demo-vis(inst_rgb_depth).jpg", result.vis()[..., ::-1])

Open ./demo-vis(inst_rgb_depth).jpg:

2. YCB Demo

Inculding domain randomization for background, light, and distractor.

mkdir ycb_demo
cd ycb_demo/

# prepare demo code and data
git clone https://github.com/DIYer22/bpycv
git clone https://github.com/DIYer22/bpycv_example_data

cd bpycv/example/

blender -b -P ycb_demo.py

cd dataset/vis/
ls .  # visualize result here

Open 0.jpg:

(instance_map | RGB | depth)

YCB demo code: example/ycb_demo.py

3. 6DoF Pose Demo

Generate and visualize 6DoF pose GT: example/6d_pose_demo.py

▮ Tips

Blender may can't direct load .obj and .dea file from YCB and ShapeNet dataset.
It's better to transefer and format using meshlabserver by run meshlabserver -i raw.obj -o for_blender.obj -m wt

suggestion and pull request are welcome 😊