File size: 3,781 Bytes
117e206 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | from dataclasses import dataclass
from pathlib import Path
import cv2
import os
import numpy as np
import torch
from monai.data.video_dataset import VideoFileDataset
from monai.visualize.utils import blend_images
from tqdm import tqdm
from surg_seg.Datasets.VideoDatasets import CombinedVidDataset, VidDataset
# from surg_seg.Datasets.ImageDataset import ImageDataset
from surg_seg.Networks.Models import FlexibleUnet1InferencePipe, AbstractInferencePipe
@dataclass
class VideoCreator:
fps: float
def __post_init__(self):
self.get_codec()
self.fourcc = cv2.VideoWriter_fourcc(*self.codec)
def create_video(
self,
model_pipe: FlexibleUnet1InferencePipe,
output_file,
ds: CombinedVidDataset,
check_codec=True,
):
if check_codec:
self.check_codec
print(f"{len(ds)} frames @ {self.fps} fps: {output_file}...")
for idx in tqdm(range(len(ds))):
img = ds[idx]["image"]
inferred_single_ch = model_pipe.infer_from_monai_tensor(img)
blended = blend_images(img, inferred_single_ch, cmap="viridis", alpha=0.8)
if idx == 0:
width_height = blended.shape[1:][::-1]
video = cv2.VideoWriter(output_file, self.fourcc, self.fps, width_height)
blended = (np.moveaxis(blended, 0, -1) * 254).astype(np.uint8)
blended = cv2.cvtColor(blended, cv2.COLOR_RGB2BGR)
video.write(blended)
video.release()
if not os.path.isfile(output_file):
raise RuntimeError("video not created:", output_file)
print("Success!")
def get_codec(self):
codecs = VideoFileDataset.get_available_codecs()
self.codec, self.ext = next(iter(codecs.items()))
print(self.codec, self.ext)
def check_codec(self):
codec_success = cv2.VideoWriter().open("test" + self.ext, self.fourcc, 1, (10, 10))
if not codec_success:
raise RuntimeError("failed to open video.")
os.remove("test" + self.ext)
def config1():
"""Config when doing inference Annie's folder structure"""
# path_to_weights = Path("./assets/weights/myweights_image_all_datasets/myweights.pt")
path_to_weights = Path("assets/weights/myweights_3d_med_2_all_ds3/myweights.pt")
## Data loading
rec_num = 1
vid_root = Path(
f"/home/juan1995/research_juan/accelnet_grant/data/phantom2_data_processed/rec{rec_num:02d}/"
)
vid_filepath = vid_root / f"raw/rec{rec_num:02d}_seg_raw.avi"
output_path = vid_root / "inferred.mp4"
return path_to_weights, vid_filepath, output_path
def config2():
"""Config with simple folder structure."""
path_to_weights = Path("assets/weights/myweights_3d_med_2_all_ds3/myweights.pt")
## Data loading
# vid_filepath = Path(
# "/home/juan1995/research_juan/accelnet_grant/data/dVRK_data_processed/rec03_right.avi"
# )
vid_filepath = Path(
"/home/juan1995/research_juan/accelnet_grant/data/zed_camera_processed/rec04/2023-04-17_18-35-37_rightXX.avi"
)
output_path = vid_filepath.parent / (vid_filepath.with_suffix("").name + "_inferred.mp4")
return path_to_weights, vid_filepath, output_path
def main():
device = "cuda"
# Choose which config to use config1() or config2()
path_to_weights, vid_filepath, output_path = config2()
model_pipe = FlexibleUnet1InferencePipe(path_to_weights, device, out_channels=5)
ds = VidDataset(vid_filepath)
# create video
fps = ds.ds_img.get_fps()
print(f"fps {fps}")
video_creator = VideoCreator(fps)
with torch.no_grad():
video_creator.create_video(model_pipe, str(output_path), ds)
if __name__ == "__main__":
main()
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