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import argparse
import logging
import os
import random
import cv2
import torch
import yt_dlp
from mivolo.data.data_reader import InputType, get_all_files, get_input_type
from mivolo.predictor import Predictor
from timm.utils import setup_default_logging
_logger = logging.getLogger("inference")
def get_direct_video_url(video_url):
ydl_opts = {
"format": "bestvideo",
"quiet": True, # Suppress terminal output
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(video_url, download=False)
if "url" in info_dict:
direct_url = info_dict["url"]
resolution = (info_dict["width"], info_dict["height"])
fps = info_dict["fps"]
yid = info_dict["id"]
return direct_url, resolution, fps, yid
return None, None, None, None
def get_local_video_info(vid_uri):
cap = cv2.VideoCapture(vid_uri)
if not cap.isOpened():
raise ValueError(f"Failed to open video source {vid_uri}")
res = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fps = cap.get(cv2.CAP_PROP_FPS)
return res, fps
def get_random_frames(cap, num_frames):
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = random.sample(range(total_frames), num_frames)
frames = []
for idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if ret:
frames.append(frame)
return frames
def get_parser():
parser = argparse.ArgumentParser(description="PyTorch MiVOLO Inference")
parser.add_argument("--input", type=str, default=None, required=True, help="image file or folder with images")
parser.add_argument("--output", type=str, default=None, required=True, help="folder for output results")
parser.add_argument("--detector-weights", type=str, default=None, required=True, help="Detector weights (YOLOv8).")
parser.add_argument("--checkpoint", default="", type=str, required=True, help="path to mivolo checkpoint")
parser.add_argument(
"--with-persons", action="store_true", default=False, help="If set model will run with persons, if available"
)
parser.add_argument(
"--disable-faces", action="store_true", default=False, help="If set model will use only persons if available"
)
parser.add_argument("--draw", action="store_true", default=False, help="If set, resulted images will be drawn")
parser.add_argument("--device", default="cuda", type=str, help="Device (accelerator) to use.")
return parser
def main():
parser = get_parser()
setup_default_logging()
args = parser.parse_args()
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
os.makedirs(args.output, exist_ok=True)
predictor = Predictor(args, verbose=True)
input_type = get_input_type(args.input)
if input_type == InputType.Video or input_type == InputType.VideoStream:
if "youtube" in args.input:
args.input, res, fps, yid = get_direct_video_url(args.input)
if not args.input:
raise ValueError(f"Failed to get direct video url {args.input}")
else:
cap = cv2.VideoCapture(args.input)
if not cap.isOpened():
raise ValueError(f"Failed to open video source {args.input}")
# Extract 4-5 random frames from the video
random_frames = get_random_frames(cap, num_frames=5)
age_list = []
for frame in random_frames:
detected_objects, out_im, age = predictor.recognize(frame)
age_list.append(age[0])
if args.draw:
bname = os.path.splitext(os.path.basename(args.input))[0]
filename = os.path.join(args.output, f"out_{bname}.jpg")
cv2.imwrite(filename, out_im)
_logger.info(f"Saved result to {filename}")
# Calculate and print average age
avg_age = sum(age_list) / len(age_list) if age_list else 0
print(f"Age list: {age_list}")
print(f"Average age: {avg_age:.2f}")
absolute_age = round(abs(avg_age))
# Define the range
lower_bound = absolute_age - 2
upper_bound = absolute_age + 2
return absolute_age, lower_bound, upper_bound
elif input_type == InputType.Image:
image_files = get_all_files(args.input) if os.path.isdir(args.input) else [args.input]
for img_p in image_files:
img = cv2.imread(img_p)
detected_objects, out_im, age = predictor.recognize(img)
if args.draw:
bname = os.path.splitext(os.path.basename(img_p))[0]
filename = os.path.join(args.output, f"out_{bname}.jpg")
cv2.imwrite(filename, out_im)
_logger.info(f"Saved result to {filename}")
if __name__ == "__main__":
absolute_age, lower_bound, upper_bound = main()
# Output the results in the desired format
print(f"Absolute Age: {absolute_age}")
print(f"Range: {lower_bound} - {upper_bound}") |