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Add Detectron2 support for inference in the wild
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# Copyright (c) 2018-present, Facebook, Inc. | ||
# All rights reserved. | ||
# | ||
# This source code is licensed under the license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
# | ||
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"""Perform inference on a single video or all videos with a certain extension | ||
(e.g., .mp4) in a folder. | ||
""" | ||
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import detectron2 | ||
from detectron2.utils.logger import setup_logger | ||
from detectron2.config import get_cfg | ||
from detectron2 import model_zoo | ||
from detectron2.engine import DefaultPredictor | ||
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import subprocess as sp | ||
import numpy as np | ||
import time | ||
import argparse | ||
import sys | ||
import os | ||
import glob | ||
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def parse_args(): | ||
parser = argparse.ArgumentParser(description='End-to-end inference') | ||
parser.add_argument( | ||
'--cfg', | ||
dest='cfg', | ||
help='cfg model file (/path/to/model_config.yaml)', | ||
default=None, | ||
type=str | ||
) | ||
parser.add_argument( | ||
'--output-dir', | ||
dest='output_dir', | ||
help='directory for visualization pdfs (default: /tmp/infer_simple)', | ||
default='/tmp/infer_simple', | ||
type=str | ||
) | ||
parser.add_argument( | ||
'--image-ext', | ||
dest='image_ext', | ||
help='image file name extension (default: mp4)', | ||
default='mp4', | ||
type=str | ||
) | ||
parser.add_argument( | ||
'im_or_folder', help='image or folder of images', default=None | ||
) | ||
if len(sys.argv) == 1: | ||
parser.print_help() | ||
sys.exit(1) | ||
return parser.parse_args() | ||
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def get_resolution(filename): | ||
command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', | ||
'-show_entries', 'stream=width,height', '-of', 'csv=p=0', filename] | ||
pipe = sp.Popen(command, stdout=sp.PIPE, bufsize=-1) | ||
for line in pipe.stdout: | ||
w, h = line.decode().strip().split(',') | ||
return int(w), int(h) | ||
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def read_video(filename): | ||
w, h = get_resolution(filename) | ||
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command = ['ffmpeg', | ||
'-i', filename, | ||
'-f', 'image2pipe', | ||
'-pix_fmt', 'bgr24', | ||
'-vsync', '0', | ||
'-vcodec', 'rawvideo', '-'] | ||
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pipe = sp.Popen(command, stdout=sp.PIPE, bufsize=-1) | ||
while True: | ||
data = pipe.stdout.read(w*h*3) | ||
if not data: | ||
break | ||
yield np.frombuffer(data, dtype='uint8').reshape((h, w, 3)) | ||
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def main(args): | ||
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cfg = get_cfg() | ||
cfg.merge_from_file(model_zoo.get_config_file(args.cfg)) | ||
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 | ||
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(args.cfg) | ||
predictor = DefaultPredictor(cfg) | ||
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if os.path.isdir(args.im_or_folder): | ||
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext) | ||
else: | ||
im_list = [args.im_or_folder] | ||
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for video_name in im_list: | ||
out_name = os.path.join( | ||
args.output_dir, os.path.basename(video_name) | ||
) | ||
print('Processing {}'.format(video_name)) | ||
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boxes = [] | ||
segments = [] | ||
keypoints = [] | ||
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for frame_i, im in enumerate(read_video(video_name)): | ||
t = time.time() | ||
outputs = predictor(im)['instances'].to('cpu') | ||
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print('Frame {} processed in {:.3f}s'.format(frame_i, time.time() - t)) | ||
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has_bbox = False | ||
if outputs.has('pred_boxes'): | ||
bbox_tensor = outputs.pred_boxes.tensor.numpy() | ||
if len(bbox_tensor) > 0: | ||
has_bbox = True | ||
scores = outputs.scores.numpy()[:, None] | ||
bbox_tensor = np.concatenate((bbox_tensor, scores), axis=1) | ||
if has_bbox: | ||
kps = outputs.pred_keypoints.numpy() | ||
kps_xy = kps[:, :, :2] | ||
kps_prob = kps[:, :, 2:3] | ||
kps_logit = np.zeros_like(kps_prob) # Dummy | ||
kps = np.concatenate((kps_xy, kps_logit, kps_prob), axis=2) | ||
kps = kps.transpose(0, 2, 1) | ||
else: | ||
kps = [] | ||
bbox_tensor = [] | ||
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# Mimic Detectron1 format | ||
cls_boxes = [[], bbox_tensor] | ||
cls_keyps = [[], kps] | ||
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boxes.append(cls_boxes) | ||
segments.append(None) | ||
keypoints.append(cls_keyps) | ||
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# Video resolution | ||
metadata = { | ||
'w': im.shape[1], | ||
'h': im.shape[0], | ||
} | ||
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np.savez_compressed(out_name, boxes=boxes, segments=segments, keypoints=keypoints, metadata=metadata) | ||
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if __name__ == '__main__': | ||
setup_logger() | ||
args = parse_args() | ||
main(args) |