# Copyright (c) Facebook, Inc. and its affiliates. import argparse import glob import json import multiprocessing as mp import os import tempfile import time import warnings from collections import abc import cv2 import numpy as np import tqdm from detectron2.config import LazyConfig, get_cfg from detectron2.data.detection_utils import read_image from detectron2.evaluation.coco_evaluation import instances_to_coco_json # from detectron2.projects.deeplab import add_deeplab_config # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config from detectron2.utils.logger import setup_logger from predictor_lazy import VisualizationDemo # constants WINDOW_NAME = "APE" def setup_cfg(args): # load config from file and command-line arguments cfg = LazyConfig.load(args.config_file) cfg = LazyConfig.apply_overrides(cfg, args.opts) if "output_dir" in cfg.model: cfg.model.output_dir = cfg.train.output_dir if "model_vision" in cfg.model and "output_dir" in cfg.model.model_vision: cfg.model.model_vision.output_dir = cfg.train.output_dir if "train" in cfg.dataloader: if isinstance(cfg.dataloader.train, abc.MutableSequence): for i in range(len(cfg.dataloader.train)): if "output_dir" in cfg.dataloader.train[i].mapper: cfg.dataloader.train[i].mapper.output_dir = cfg.train.output_dir else: if "output_dir" in cfg.dataloader.train.mapper: cfg.dataloader.train.mapper.output_dir = cfg.train.output_dir if "model_vision" in cfg.model: cfg.model.model_vision.test_score_thresh = args.confidence_threshold else: cfg.model.test_score_thresh = args.confidence_threshold # default_setup(cfg, args) setup_logger(name="ape") setup_logger(name="timm") return cfg def get_parser(): parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") parser.add_argument( "--config-file", default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", metavar="FILE", help="path to config file", ) parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") parser.add_argument("--video-input", help="Path to video file.") parser.add_argument( "--input", nargs="+", help="A list of space separated input images; " "or a single glob pattern such as 'directory/*.jpg'", ) parser.add_argument( "--output", help="A file or directory to save output visualizations. " "If not given, will show output in an OpenCV window.", ) parser.add_argument( "--confidence-threshold", type=float, default=0.5, help="Minimum score for instance predictions to be shown", ) parser.add_argument( "--opts", help="Modify config options using the command-line 'KEY VALUE' pairs", default=[], nargs=argparse.REMAINDER, ) parser.add_argument("--text-prompt", default=None) parser.add_argument("--with-box", action="store_true", help="show box of instance") parser.add_argument("--with-mask", action="store_true", help="show mask of instance") parser.add_argument("--with-sseg", action="store_true", help="show mask of class") return parser def test_opencv_video_format(codec, file_ext): with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: filename = os.path.join(dir, "test_file" + file_ext) writer = cv2.VideoWriter( filename=filename, fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(30), frameSize=(10, 10), isColor=True, ) [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] writer.release() if os.path.isfile(filename): return True return False if __name__ == "__main__": mp.set_start_method("spawn", force=True) args = get_parser().parse_args() setup_logger(name="fvcore") setup_logger(name="ape") logger = setup_logger() logger.info("Arguments: " + str(args)) cfg = setup_cfg(args) if args.video_input: demo = VisualizationDemo(cfg, parallel=True, args=args) else: demo = VisualizationDemo(cfg, args=args) if args.input: if len(args.input) == 1: args.input = glob.glob(os.path.expanduser(args.input[0]), recursive=True) assert args.input, "The input path(s) was not found" for path in tqdm.tqdm(args.input, disable=not args.output): # use PIL, to be consistent with evaluation try: img = read_image(path, format="BGR") except Exception as e: print("*" * 60) print("fail to open image: ", e) print("*" * 60) continue start_time = time.time() predictions, visualized_output, visualized_outputs, metadata = demo.run_on_image( img, text_prompt=args.text_prompt, with_box=args.with_box, with_mask=args.with_mask, with_sseg=args.with_sseg, ) logger.info( "{}: {} in {:.2f}s".format( path, "detected {} instances".format(len(predictions["instances"])) if "instances" in predictions else "finished", time.time() - start_time, ) ) if args.output: if os.path.isdir(args.output): assert os.path.isdir(args.output), args.output out_filename = os.path.join(args.output, os.path.basename(path)) else: assert len(args.input) == 1, "Please specify a directory with args.output" out_filename = args.output out_filename = out_filename.replace(".webp", ".png") out_filename = out_filename.replace(".crdownload", ".png") out_filename = out_filename.replace(".jfif", ".png") visualized_output.save(out_filename) for i in range(len(visualized_outputs)): out_filename = ( os.path.join(args.output, os.path.basename(path)) + "." + str(i) + ".png" ) visualized_outputs[i].save(out_filename) # import pickle # with open(out_filename + ".pkl", "wb") as outp: # pickle.dump(predictions, outp, pickle.HIGHEST_PROTOCOL) if "instances" in predictions: results = instances_to_coco_json( predictions["instances"].to(demo.cpu_device), path ) for result in results: result["category_name"] = metadata.thing_classes[result["category_id"]] result["image_name"] = result["image_id"] with open(out_filename + ".json", "w") as outp: json.dump(results, outp) else: cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) if cv2.waitKey(0) == 27: break # esc to quit elif args.webcam: assert args.input is None, "Cannot have both --input and --webcam!" assert args.output is None, "output not yet supported with --webcam!" cam = cv2.VideoCapture(0) for vis in tqdm.tqdm(demo.run_on_video(cam)): cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, vis) if cv2.waitKey(1) == 27: break # esc to quit cam.release() cv2.destroyAllWindows() elif args.video_input: video = cv2.VideoCapture(args.video_input) width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frames_per_second = video.get(cv2.CAP_PROP_FPS) num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) basename = os.path.basename(args.video_input) codec, file_ext = ( ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") ) codec, file_ext = "mp4v", ".mp4" if codec == ".mp4v": warnings.warn("x264 codec not available, switching to mp4v") if args.output: if os.path.isdir(args.output): output_fname = os.path.join(args.output, basename) output_fname = os.path.splitext(output_fname)[0] + file_ext else: output_fname = args.output assert not os.path.isfile(output_fname), output_fname output_file = cv2.VideoWriter( filename=output_fname, # some installation of opencv may not support x264 (due to its license), # you can try other format (e.g. MPEG) fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(frames_per_second), frameSize=(width, height), isColor=True, ) # i = 0 assert os.path.isfile(args.video_input) for vis_frame, predictions in tqdm.tqdm(demo.run_on_video(video), total=num_frames): if args.output: output_file.write(vis_frame) # import pickle # with open(output_fname + "." + str(i) + ".pkl", "wb") as outp: # pickle.dump(predictions, outp, pickle.HIGHEST_PROTOCOL) # i += 1 else: cv2.namedWindow(basename, cv2.WINDOW_NORMAL) cv2.imshow(basename, vis_frame) if cv2.waitKey(1) == 27: break # esc to quit video.release() if args.output: output_file.release() else: cv2.destroyAllWindows()