76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
# based on https://github.com/DIYer22/bpycv/blob/c576e01622d87eb3534f73bf1a5686bd2463de97/example/ycb_demo.py
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import bpy
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import bpycv
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import os
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import glob
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import random
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example_data_dir = os.environ['BPY_EXAMPLE_DATA']
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models = sorted(glob.glob(os.path.join(example_data_dir, "model", "*", "*.obj")))
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cat_id_to_model_path = dict(enumerate(sorted(models), 1))
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distractors = sorted(glob.glob(os.path.join(example_data_dir, "distractor", "*.obj")))
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bpycv.clear_all()
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bpy.context.scene.frame_set(1)
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bpy.context.scene.render.engine = "CYCLES"
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bpy.context.scene.cycles.samples = 32
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bpy.context.scene.render.resolution_y = 1024
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bpy.context.scene.render.resolution_x = 1024
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# A transparency stage for holding rigid body
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stage = bpycv.add_stage(transparency=True)
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bpycv.set_cam_pose(cam_radius=1, cam_deg=45)
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hdri_dir = os.path.join(example_data_dir, "background_and_light")
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hdri_manager = bpycv.HdriManager(
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hdri_dir=hdri_dir, download=False
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) # if download is True, will auto download .hdr file from HDRI Haven
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hdri_path = hdri_manager.sample()
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bpycv.load_hdri_world(hdri_path, random_rotate_z=True)
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# load 5 objects
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for index in range(5):
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cat_id = random.choice(list(cat_id_to_model_path))
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model_path = cat_id_to_model_path[cat_id]
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obj = bpycv.load_obj(model_path)
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obj.location = (
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random.uniform(-0.2, 0.2),
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random.uniform(-0.2, 0.2),
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random.uniform(0.1, 0.3),
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)
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obj.rotation_euler = [random.uniform(-3.1415, 3.1415) for _ in range(3)]
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# set each instance a unique inst_id, which is used to generate instance annotation.
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obj["inst_id"] = cat_id * 1000 + index
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with bpycv.activate_obj(obj):
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bpy.ops.rigidbody.object_add()
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# load 6 distractors
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for index in range(6):
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distractor_path = random.choice(distractors)
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target_size = random.uniform(0.1, 0.3)
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distractor = bpycv.load_distractor(distractor_path, target_size=target_size)
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distractor.location = (
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random.uniform(-0.2, 0.2),
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random.uniform(-0.2, 0.2),
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random.uniform(0.1, 0.3),
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)
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distractor.rotation_euler = [random.uniform(-3.1415, 3.1415) for _ in range(3)]
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with bpycv.activate_obj(distractor):
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bpy.ops.rigidbody.object_add()
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# run pyhsic engine for 20 frames
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for i in range(20):
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bpy.context.scene.frame_set(bpy.context.scene.frame_current + 1)
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# render image, instance annoatation and depth in one line code
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result = bpycv.render_data()
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dataset_dir = "./dataset"
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result.save(dataset_dir=dataset_dir, fname="0", save_blend=True)
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print(f'Save to "{dataset_dir}"')
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print(f'Open "{dataset_dir}/vis/" to see visualize result.')
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