def bpycv_test():
result = bpycv.render_data()
cv2.imwrite(
r"D:\tmp\rgb.jpg",
result["image"][..., ::-1]) # transfer RGB image to opencv's BGR
cv2.imwrite(r"D:\tmp\inst.png", result["inst"],
[cv2.IMWRITE_PNG_COMPRESSION, 0])
cv2.imwrite(r"D:\tmp\sem.png", result["sem"],
[cv2.IMWRITE_PNG_COMPRESSION, 0])
def render_gt(current_run_base_dir, data_dir, rendering_frames):
"""Renders GT for current for all frames and saves it in gt_base_dir"""
bpy.types.ImageFormatSettings.color_depth = 16
# gt rendering
for i, frame in enumerate(rendering_frames):
logging.info(
f"Render GT frame {frame} ({i+1}/{len(rendering_frames)})")
bpy.context.scene.frame_set(frame)
result = bpycv.render_data(render_image=False, render_annotation=True)
cv2.imwrite(
os.path.join(current_run_base_dir, "all", "semantic_segmentation",
"semantic_segmentation" + str(frame) + ".png"),
result["inst"])
disparity = post_processing.generate_disparity(result["depth"],
result["inst"],
data_dir)
cv2.imwrite(
os.path.join(current_run_base_dir, "all", "disparity",
"disparity" + str(frame) + ".png"), disparity)
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
