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import argparse
import os.path
import pickle
import h5py
import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
import cv2
from SoccerNet.Evaluation.utils_calibration import SoccerPitch
from custom_extremities import CustomNetwork
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Test")
parser.add_argument(
"-s",
"--soccernet",
default="/nfs/data/soccernet/calibration/",
type=str,
help="Path to the SoccerNet-V3 dataset folder",
)
parser.add_argument(
"-p",
"--prediction",
default="/nfs/home/rhotertj/datasets/sn-calib-test_endpoints",
required=False,
type=str,
help="Path to the prediction folder",
)
parser.add_argument(
"--split",
required=False,
type=str,
default="challenge",
help="Select the split of data",
)
parser.add_argument(
"--resolution_width",
required=False,
type=int,
default=455,
help="width resolution of the images",
)
parser.add_argument(
"--resolution_height",
required=False,
type=int,
default=256,
help="height resolution of the images",
)
parser.add_argument(
"--checkpoint",
required=False,
type=str,
help="Path to the custom model checkpoint.",
)
parser.add_argument("--filter_cam", type=str, required=False)
args = parser.parse_args()
lines_palette = [0, 0, 0]
for line_class in SoccerPitch.lines_classes:
print(line_class, SoccerPitch.palette[line_class])
lines_palette.extend(SoccerPitch.palette[line_class])
print(lines_palette)
# exit(0)
dataset_dir = os.path.join(args.soccernet, args.split)
if not os.path.exists(dataset_dir):
print("Invalid dataset path !")
exit(-1)
match_info_file = os.path.join(args.soccernet, args.split, "match_info_cam_gt.json")
print(match_info_file)
if not os.path.exists(match_info_file):
exit(-1)
df = pd.read_json(match_info_file).T
if args.filter_cam:
df = df.loc[df.camera == args.filter_cam]
df["image_file"] = df.index
df = df.sort_values(by=["image_file"])
print(df)
frames = df["image_file"].tolist()
model = CustomNetwork(args.checkpoint)
image_src = []
edge_maps = np.zeros((len(frames), 1, 180, 320), dtype=np.uint8)
kernel = np.ones((4, 4), np.uint8)
with tqdm(enumerate(frames), total=len(frames), ncols=100) as t:
for i, frame in t:
output_prediction_folder = args.prediction
if not os.path.exists(output_prediction_folder):
os.makedirs(output_prediction_folder)
frame_path = os.path.join(dataset_dir, frame)
frame_index = frame.split(".")[0]
image = Image.open(frame_path)
semlines = model.forward(image)
# print(semlines.shape, np.unique(semlines))
# set class 9-15 (goal parts) to background
mask_goal = (semlines >= 9) & (semlines <= 15)
semlines[mask_goal] = 0
mask = Image.fromarray(semlines.astype(np.uint8)).convert("P")
mask.putpalette(lines_palette)
# to binary edge map
mask = np.asarray(mask.convert("L"))
mask[mask > 0] = 255
mask = Image.fromarray(mask)
mask = mask.resize((320, 180), resample=Image.NEAREST)
# expected linewith @ 720p resulution -> 4px
mask = np.asarray(mask)
# print(mask.shape)
mask = cv2.erode(mask, kernel, iterations=1)
# assert len(np.unique(mask)) == 2 # [0, 255]
# mask_file = os.path.join(output_prediction_folder, frame)
# mask.save(mask_file)
# print(mask)
# exit(0)
edge_maps[i] = mask
image_src.append(frame)
with h5py.File(
os.path.join(output_prediction_folder, "seg_edge_maps.h5"), "w"
) as f:
f.create_dataset("edge_map", data=edge_maps)
with open(os.path.join(output_prediction_folder, "seg_image_paths.pkl"), "wb") as f:
pickle.dump(image_src, f)
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