wehub-resource-sync
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
import os
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import megengine as mge
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import megengine.functional as F
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import argparse
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import numpy as np
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import cv2
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from nets import Model
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def load_model(model_path):
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print("Loading model:", os.path.abspath(model_path))
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pretrained_dict = mge.load(model_path)
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model = Model(max_disp=256, mixed_precision=False, test_mode=True)
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model.load_state_dict(pretrained_dict["state_dict"], strict=True)
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model.eval()
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return model
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def inference(left, right, model, n_iter=20):
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print("Model Forwarding...")
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imgL = left.transpose(2, 0, 1)
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imgR = right.transpose(2, 0, 1)
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imgL = np.ascontiguousarray(imgL[None, :, :, :])
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imgR = np.ascontiguousarray(imgR[None, :, :, :])
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imgL = mge.tensor(imgL).astype("float32")
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imgR = mge.tensor(imgR).astype("float32")
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imgL_dw2 = F.nn.interpolate(
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imgL,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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imgR_dw2 = F.nn.interpolate(
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imgR,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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pred_flow_dw2 = model(imgL_dw2, imgR_dw2, iters=n_iter, flow_init=None)
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pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
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pred_disp = F.squeeze(pred_flow[:, 0, :, :]).numpy()
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return pred_disp
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="A demo to run CREStereo.")
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parser.add_argument(
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"--model_path",
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default="crestereo_eth3d.mge",
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help="The path of pre-trained MegEngine model.",
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)
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parser.add_argument(
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"--left", default="img/test/left.png", help="The path of left image."
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)
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parser.add_argument(
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"--right", default="img/test/right.png", help="The path of right image."
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)
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parser.add_argument(
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"--size",
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default="1024x1536",
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help="The image size for inference. Te default setting is 1024x1536. \
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To evaluate on ETH3D Benchmark, use 768x1024 instead.",
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)
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parser.add_argument(
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"--output", default="disparity.png", help="The path of output disparity."
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)
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args = parser.parse_args()
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assert os.path.exists(args.model_path), "The model path do not exist."
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assert os.path.exists(args.left), "The left image path do not exist."
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assert os.path.exists(args.right), "The right image path do not exist."
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model_func = load_model(args.model_path)
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left = cv2.imread(args.left)
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right = cv2.imread(args.right)
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assert left.shape == right.shape, "The input images have inconsistent shapes."
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in_h, in_w = left.shape[:2]
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print("Images resized:", args.size)
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eval_h, eval_w = [int(e) for e in args.size.split("x")]
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left_img = cv2.resize(left, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
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right_img = cv2.resize(right, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
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pred = inference(left_img, right_img, model_func, n_iter=20)
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t = float(in_w) / float(eval_w)
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disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
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disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
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disp_vis = disp_vis.astype("uint8")
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disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
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parent_path = os.path.abspath(os.path.join(args.output, os.pardir))
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if not os.path.exists(parent_path):
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os.makedirs(parent_path)
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cv2.imwrite(args.output, disp_vis)
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print("Done! Result path:", os.path.abspath(args.output))
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