# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import argparse import glob import multiprocessing as mp import os import time import cv2 import tqdm import matplotlib.pyplot as plt from detectron2.config import get_cfg from detectron2.data.detection_utils import read_image from detectron2.utils.logger import setup_logger from predictor import VisualizationDemo from stoma.modeling import KRCNNConvHead from stoma.data.builtin import register_stoma from record import ( record_predictions, AnnotationStore, draw_width_predictions ) from post_processing import remove_outliers_from_records # constants WINDOW_NAME = "Stoma detections" # Hack for open MP on MACOS os.environ["KMP_DUPLICATE_LIB_OK"] = "True" def setup_cfg(args): # load config from file and command-line arguments cfg = get_cfg() # a dirty fix for the keypoint resolution config cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = (14, 14) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) # Set score_threshold for builtin models cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = ( args.confidence_threshold ) cfg.MODEL.DEVICE = "cuda" if args.gpu_inference else "cpu" cfg.freeze() return cfg def get_parser(): parser = argparse.ArgumentParser(description="Detectron2 demo for builtin models") 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( "--annotations", default=None, help="Path to file containing image ground truth annotations", ) 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( "--gpu-inference", help="Use GPU to preform inference", action="store_true", ) return parser if __name__ == "__main__": mp.set_start_method("spawn", force=True) args = get_parser().parse_args() setup_logger(name="fvcore") logger = setup_logger() logger.info("Arguments: " + str(args)) cfg = setup_cfg(args) demo = VisualizationDemo(cfg) # Stores annotation information if not args.annotations is None: stoma_annotations = AnnotationStore(args.annotations) else: stoma_annotations = None if args.input: if os.path.isdir(args.input[0]): register_stoma("datasets") args.input = [ os.path.join(args.input[0], fname) for fname in os.listdir(args.input[0]) ] if len(args.input) == 1: args.input = glob.glob(os.path.expanduser(args.input[0])) 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 img = read_image(path, format="BGR") start_time = time.time() predictions, visualized_output = demo.run_on_image(img) 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 not os.path.isdir(args.output): os.makedirs(args.output) assert os.path.isdir(args.output), args.output out_filename = os.path.join(args.output, os.path.basename(path)) # Hook to write predictions for evaluation predictions = record_predictions( predictions["instances"], out_filename, stoma_annotations ) mpl_figure = draw_width_predictions(visualized_output, predictions) mpl_figure.savefig(out_filename, dpi=400, bbox_inches = 'tight', pad_inches = 0) plt.close(mpl_figure) 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 # Experiment wide filtering remove_outliers_from_records(args.output) 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) 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] + ".mkv" 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(*"x264"), fps=float(frames_per_second), frameSize=(width, height), isColor=True, ) assert os.path.isfile(args.video_input) for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): if args.output: output_file.write(vis_frame) 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()