115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
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# https://blender.stackexchange.com/questions/38009/3x4-camera-matrix-from-blender-camera
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import bpy
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from mathutils import Matrix
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from mathutils import Vector
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#---------------------------------------------------------------
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# 3x4 P matrix from Blender camera
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#---------------------------------------------------------------
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# Build intrinsic camera parameters from Blender camera data
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#
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# See notes on this in
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# blender.stackexchange.com/questions/15102/what-is-blenders-camera-projection-matrix-model
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def get_calibration_matrix_K_from_blender(camd):
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f_in_mm = camd.lens
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scene = bpy.context.scene
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resolution_x_in_px = scene.render.resolution_x
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resolution_y_in_px = scene.render.resolution_y
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scale = scene.render.resolution_percentage / 100
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sensor_width_in_mm = camd.sensor_width
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sensor_height_in_mm = camd.sensor_height
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pixel_aspect_ratio = scene.render.pixel_aspect_x / scene.render.pixel_aspect_y
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if (camd.sensor_fit == 'VERTICAL'):
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# the sensor height is fixed (sensor fit is horizontal),
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# the sensor width is effectively changed with the pixel aspect ratio
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s_u = resolution_x_in_px * scale / sensor_width_in_mm / pixel_aspect_ratio
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s_v = resolution_y_in_px * scale / sensor_height_in_mm
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else: # 'HORIZONTAL' and 'AUTO'
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# the sensor width is fixed (sensor fit is horizontal),
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# the sensor height is effectively changed with the pixel aspect ratio
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pixel_aspect_ratio = scene.render.pixel_aspect_x / scene.render.pixel_aspect_y
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s_u = resolution_x_in_px * scale / sensor_width_in_mm
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s_v = resolution_y_in_px * scale * pixel_aspect_ratio / sensor_height_in_mm
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# Parameters of intrinsic calibration matrix K
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alpha_u = f_in_mm * s_u
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alpha_v = f_in_mm * s_v
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u_0 = resolution_x_in_px * scale / 2
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v_0 = resolution_y_in_px * scale / 2
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skew = 0 # only use rectangular pixels
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K = Matrix(
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((alpha_u, skew, u_0),
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( 0 , alpha_v, v_0),
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( 0 , 0, 1 )))
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return K
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# Returns camera rotation and translation matrices from Blender.
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#
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# There are 3 coordinate systems involved:
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# 1. The World coordinates: "world"
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# - right-handed
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# 2. The Blender camera coordinates: "bcam"
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# - x is horizontal
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# - y is up
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# - right-handed: negative z look-at direction
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# 3. The desired computer vision camera coordinates: "cv"
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# - x is horizontal
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# - y is down (to align to the actual pixel coordinates
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# used in digital images)
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# - right-handed: positive z look-at direction
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def get_3x4_RT_matrix_from_blender(cam):
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# bcam stands for blender camera
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R_bcam2cv = Matrix(
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((1, 0, 0),
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(0, -1, 0),
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(0, 0, -1)))
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# Transpose since the rotation is object rotation,
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# and we want coordinate rotation
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# R_world2bcam = cam.rotation_euler.to_matrix().transposed()
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# T_world2bcam = -1*R_world2bcam * location
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#
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# Use matrix_world instead to account for all constraints
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location, rotation = cam.matrix_world.decompose()[0:2]
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R_world2bcam = rotation.to_matrix().transposed()
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# Convert camera location to translation vector used in coordinate changes
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# T_world2bcam = -1*R_world2bcam*cam.location
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# Use location from matrix_world to account for constraints:
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T_world2bcam = -1*R_world2bcam @ location
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# Build the coordinate transform matrix from world to computer vision camera
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# NOTE: Use * instead of @ here for older versions of Blender
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# TODO: detect Blender version
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R_world2cv = R_bcam2cv@R_world2bcam
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T_world2cv = R_bcam2cv@T_world2bcam
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# put into 3x4 matrix
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RT = Matrix((
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R_world2cv[0][:] + (T_world2cv[0],),
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R_world2cv[1][:] + (T_world2cv[1],),
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R_world2cv[2][:] + (T_world2cv[2],)
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))
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return RT
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def get_3x4_P_matrix_from_blender(cam):
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K = get_calibration_matrix_K_from_blender(cam.data)
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RT = get_3x4_RT_matrix_from_blender(cam)
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return K@RT, K, RT
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def run_script(scene):
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# projection_matrix = scene.camera.matrix_world
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projection_matrix, _, _ = get_3x4_P_matrix_from_blender(scene.camera)
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with open('/home/damien/Documents/3A/projet-be/imgs/torus/matrices.txt', 'a') as f:
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f.write(projection_matrix.__repr__() + '\n\n')
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f = open('/home/damien/Documents/3A/projet-be/imgs/torus/matrices.txt', 'w')
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f.close()
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bpy.app.handlers.frame_change_post.append(run_script)
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