187 lines
6.2 KiB
Python
187 lines
6.2 KiB
Python
import json
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from pathlib import Path
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import logging
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import torch
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from ultralytics import YOLO
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import cv2
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from dodgeballtracker.helper import create_video_writer, iter_frames
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import matplotlib
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import numpy as np
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from boxmot import DeepOcSort as DeepOCSORT
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logger = logging.getLogger(__name__)
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Color = tuple[int, int, int]
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def get_best_available_torch_device():
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if torch.cuda.is_available():
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logger.debug("Using the cuda (gpu) torch device.")
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return 'cuda'
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if torch.backends.mps.is_available():
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logger.debug("Using the mps (apple silicon) torch device.")
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return 'mps'
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logger.debug("Default to cpu (no apple silicon or cuda) torch device.")
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return 'cpu'
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class DetectedObjectBox:
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def __init__(self, min_threshold: float, color: Color):
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self.min_threshold = min_threshold
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self.color = color
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def __lt__(self, other):
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return self.min_threshold < other.min_threshold
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@classmethod
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def from_viridis(cls, min_threshold):
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cm = matplotlib.colormaps['viridis']
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color: Color = tuple((int(255 * cm(min_threshold)[i]) for i in range(3)))
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return cls(min_threshold, color)
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def get_court_perspective_transform():
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"""
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"""
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empirical_top_left = np.float32([416, 175])
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empirical_top_right = np.float32([905, 173])
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empirical_bottom_left = np.float32([51, 702])
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empirical_bottom_right = np.float32([1188, 695])
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court_top_left = np.float32([0, 0])
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court_top_right = np.float32([9, 0])
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court_bottom_left = np.float32([0, 18])
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court_bottom_right = np.float32([9, 18])
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empirical_source = np.float32([empirical_top_left,
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empirical_top_right,
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empirical_bottom_left,
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empirical_bottom_right])
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pixel_per_meter = 50
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empirical_target = np.float32([court_top_left,
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court_top_right,
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court_bottom_left,
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court_bottom_right]) * pixel_per_meter
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return cv2.getPerspectiveTransform(empirical_source, empirical_target)
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class DodgeballTracker:
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def __init__(self,
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input_file: Path,
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output_file: Path,
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detected_box_objects: list[DetectedObjectBox],
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transformation_matrix):
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self.input_path = input_file
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self.output_path = output_file
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self.output_json_path = output_file.with_suffix(".json")
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self.detected_box_objects = sorted(detected_box_objects, reverse=True)
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self.transformation_matrix = transformation_matrix
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self.points = []
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def write_frame(self, writer, frame) -> None:
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cv2.imshow("Frame", frame)
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writer.write(frame)
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def extract_box(self, data) -> tuple[int, int, int, int]:
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return int(data[0]), int(data[1]), int(data[2]), int(data[3])
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def print_box(self, data, frame_idx, frame_ts):
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xmin, ymin, xmax, ymax = self.extract_box(data)
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xavg = (xmax + xmin) / 2
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v = np.array([xavg, ymax, 1])
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ret = self.transformation_matrix @ v
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x, y = ret[0] / ret[2], ret[1] / ret[2]
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object_id = data[-1]
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self.points.append({"x": x,
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"y": y,
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"frameIndex": frame_idx,
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"frameTimestamp": frame_ts,
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"objectId": int(object_id)
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})
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def print_tracker_data(self, tracker_data, frame_idx, frame_ts):
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for d in tracker_data:
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self.print_box(d, frame_idx, frame_ts)
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def plot_box_to_frame(self, data, frame):
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confidence = float(data[4])
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for detected_box_object in self.detected_box_objects:
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if detected_box_object.min_threshold < confidence:
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xmin, ymin, xmax, ymax = self.extract_box(data)
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cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), detected_box_object.color, 2)
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return True
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return False
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def run_model_on_frame(self, model, frame):
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return model(frame)[0]
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def crazy_experiment(self, frame):
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cv2.putText(frame,
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"Hello world",
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(50, 50),
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cv2.FONT_HERSHEY_SIMPLEX,
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2,
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(0, 255, 0), 8)
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cv2.line(frame, (0, 0), (511, 511), (255, 0, 0), 5)
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pts = np.array([[50, 50], [500, 50], [500, 500], [50, 500], [50, 50]], np.int32)
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pts = pts.reshape((-1, 1, 2))
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cv2.polylines(frame, pts, True, (0, 255, 255), 8)
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def persist_points(self):
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with open(self.output_json_path, "wt") as file_:
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json.dump({"points": self.points}, file_, indent=4)
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def _get_tracker(self):
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return DeepOCSORT(
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Path('lmbn_n_cuhk03_d.pt'), # which ReID model to use
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torch.device(get_best_available_torch_device()),
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True,
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)
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def _get_detection_model(self):
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"""
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load the pre-trained YOLOv8n model
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"""
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return YOLO("yolo11n.pt", verbose=True)
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def main(self):
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# define some constants
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print("started")
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video_cap = cv2.VideoCapture(str(self.input_path))
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writer = create_video_writer(video_cap, str(self.output_path))
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tracker = self._get_tracker()
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# load the pre-trained YOLOv8n model
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model = self._get_detection_model()
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for i, (frame, ts) in enumerate(iter_frames(video_cap)):
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# run the YOLO model on the frame
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res = []
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detections = self.run_model_on_frame(model, frame)
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for data in detections.boxes.data.tolist():
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is_good_box = self.plot_box_to_frame(data, frame)
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if is_good_box:
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res.append(data)
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# show the frame to our screen
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print("tracker?")
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data_ = tracker.update(np.array(res), frame)
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self.print_tracker_data(data_, i, ts)
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self.write_frame(writer, frame)
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if cv2.waitKey(1) == ord("q"):
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break
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print("fini")
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video_cap.release()
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writer.release()
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cv2.destroyAllWindows()
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self.persist_points()
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print("finished")
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