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142 lines
5.1 KiB
142 lines
5.1 KiB
import numpy as np
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from common.transformations.camera import (FULL_FRAME_SIZE, eon_focal_length,
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get_view_frame_from_road_frame,
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vp_from_ke)
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# segnet
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SEGNET_SIZE = (512, 384)
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segnet_frame_from_camera_frame = np.array([
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[float(SEGNET_SIZE[0])/FULL_FRAME_SIZE[0], 0., ],
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[ 0., float(SEGNET_SIZE[1])/FULL_FRAME_SIZE[1]]])
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# model
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MODEL_INPUT_SIZE = (320, 160)
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MODEL_YUV_SIZE = (MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1] * 3 // 2)
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MODEL_CX = MODEL_INPUT_SIZE[0]/2.
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MODEL_CY = 21.
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model_zoom = 1.25
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model_height = 1.22
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# canonical model transform
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model_intrinsics = np.array(
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[[ eon_focal_length / model_zoom, 0. , MODEL_CX],
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[ 0. , eon_focal_length / model_zoom, MODEL_CY],
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[ 0. , 0. , 1.]])
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# MED model
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MEDMODEL_INPUT_SIZE = (512, 256)
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MEDMODEL_YUV_SIZE = (MEDMODEL_INPUT_SIZE[0], MEDMODEL_INPUT_SIZE[1] * 3 // 2)
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MEDMODEL_CY = 47.6
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medmodel_zoom = 1.
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medmodel_intrinsics = np.array(
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[[ eon_focal_length / medmodel_zoom, 0. , 0.5 * MEDMODEL_INPUT_SIZE[0]],
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[ 0. , eon_focal_length / medmodel_zoom, MEDMODEL_CY],
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[ 0. , 0. , 1.]])
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# BIG model
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BIGMODEL_INPUT_SIZE = (1024, 512)
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BIGMODEL_YUV_SIZE = (BIGMODEL_INPUT_SIZE[0], BIGMODEL_INPUT_SIZE[1] * 3 // 2)
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bigmodel_zoom = 1.
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bigmodel_intrinsics = np.array(
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[[ eon_focal_length / bigmodel_zoom, 0. , 0.5 * BIGMODEL_INPUT_SIZE[0]],
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[ 0. , eon_focal_length / bigmodel_zoom, 256+MEDMODEL_CY],
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[ 0. , 0. , 1.]])
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model_frame_from_road_frame = np.dot(model_intrinsics,
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get_view_frame_from_road_frame(0, 0, 0, model_height))
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bigmodel_frame_from_road_frame = np.dot(bigmodel_intrinsics,
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get_view_frame_from_road_frame(0, 0, 0, model_height))
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medmodel_frame_from_road_frame = np.dot(medmodel_intrinsics,
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get_view_frame_from_road_frame(0, 0, 0, model_height))
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model_frame_from_bigmodel_frame = np.dot(model_intrinsics, np.linalg.inv(bigmodel_intrinsics))
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medmodel_frame_from_bigmodel_frame = np.dot(medmodel_intrinsics, np.linalg.inv(bigmodel_intrinsics))
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# 'camera from model camera'
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def get_model_height_transform(camera_frame_from_road_frame, height):
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camera_frame_from_road_ground = np.dot(camera_frame_from_road_frame, np.array([
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[1, 0, 0],
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[0, 1, 0],
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[0, 0, 0],
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[0, 0, 1],
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]))
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camera_frame_from_road_high = np.dot(camera_frame_from_road_frame, np.array([
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[1, 0, 0],
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[0, 1, 0],
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[0, 0, height - model_height],
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[0, 0, 1],
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]))
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road_high_from_camera_frame = np.linalg.inv(camera_frame_from_road_high)
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high_camera_from_low_camera = np.dot(camera_frame_from_road_ground, road_high_from_camera_frame)
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return high_camera_from_low_camera
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# camera_frame_from_model_frame aka 'warp matrix'
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# was: calibration.h/CalibrationTransform
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def get_camera_frame_from_model_frame(camera_frame_from_road_frame, height=model_height):
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vp = vp_from_ke(camera_frame_from_road_frame)
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model_camera_from_model_frame = np.array([
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[model_zoom, 0., vp[0] - MODEL_CX * model_zoom],
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[ 0., model_zoom, vp[1] - MODEL_CY * model_zoom],
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[ 0., 0., 1.],
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])
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# This function is super slow, so skip it if height is very close to canonical
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# TODO: speed it up!
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if abs(height - model_height) > 0.001:
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camera_from_model_camera = get_model_height_transform(camera_frame_from_road_frame, height)
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else:
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camera_from_model_camera = np.eye(3)
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return np.dot(camera_from_model_camera, model_camera_from_model_frame)
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def get_camera_frame_from_medmodel_frame(camera_frame_from_road_frame):
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camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
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medmodel_frame_from_ground = medmodel_frame_from_road_frame[:, (0, 1, 3)]
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ground_from_medmodel_frame = np.linalg.inv(medmodel_frame_from_ground)
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camera_frame_from_medmodel_frame = np.dot(camera_frame_from_ground, ground_from_medmodel_frame)
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return camera_frame_from_medmodel_frame
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def get_camera_frame_from_bigmodel_frame(camera_frame_from_road_frame):
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camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
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bigmodel_frame_from_ground = bigmodel_frame_from_road_frame[:, (0, 1, 3)]
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ground_from_bigmodel_frame = np.linalg.inv(bigmodel_frame_from_ground)
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camera_frame_from_bigmodel_frame = np.dot(camera_frame_from_ground, ground_from_bigmodel_frame)
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return camera_frame_from_bigmodel_frame
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def get_model_frame(snu_full, camera_frame_from_model_frame, size):
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idxs = camera_frame_from_model_frame.dot(np.column_stack([np.tile(np.arange(size[0]), size[1]),
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np.tile(np.arange(size[1]), (size[0], 1)).T.flatten(),
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np.ones(size[0] * size[1])]).T).T.astype(int)
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calib_flat = snu_full[idxs[:, 1], idxs[:, 0]]
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if len(snu_full.shape) == 3:
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calib = calib_flat.reshape((size[1], size[0], 3))
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elif len(snu_full.shape) == 2:
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calib = calib_flat.reshape((size[1], size[0]))
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else:
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raise ValueError("shape of input img is weird")
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return calib
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