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from collections import Counter
from colorsys import hls_to_rgb
from copy import deepcopy
import json
import numpy as np
from fish_length import Fish_Length
from lib.fish_eye.tracker_sort import Sort
from lib.fish_eye.tracker_bytetrack import Associate
import lib
class Tracker:
def __init__(self, clip_info, algorithm=Sort, args={'max_age':1, 'min_hits':0, 'iou_threshold':0.05}, min_hits=3, reverse=False):
self.algorithm = algorithm(**args)
self.fish_ids = Counter()
self.reverse = reverse
self.min_hits = min_hits
self.json_data = deepcopy(clip_info)
if reverse:
self.frame_id = self.json_data['end_frame']
else:
self.frame_id = self.json_data['start_frame']
self.json_data['frames'] = []
# Boxes should be given in normalized [x1,y1,x2,y2,c]
def update(self, dets=np.empty((0, 5))):
new_frame_entries = []
for track in self.algorithm.update(dets):
# Match confidence with correct track
conf = 0
min_score = 1000000
if type(self.algorithm) == lib.fish_eye.tracker_sort.Sort:
for det in dets:
score = sum(abs(det[0:4] - track[0:4]))
if (score < min_score):
min_score = score
conf = det[4]
elif type(self.algorithm) == lib.fish_eye.tracker_bytetrack.Associate:
for det in dets[0]:
score = sum(abs(det[0:4] - track[0:4]))
if (score < min_score):
min_score = score
conf = det[4]
for det in dets[1]:
score = sum(abs(det[0:4] - track[0:4]))
if (score < min_score):
min_score = score
conf = det[4]
# Assign Track
self.fish_ids[int(track[4])] += 1
new_frame_entries.append({
'fish_id': int(track[4]),
'bbox': list(track[:4]),
'visible': 1,
'human_labeled': 0,
'conf': conf
})
new_frame_entries = sorted(new_frame_entries, key=lambda k: k['fish_id'])
self.json_data['frames'].append(
{
'frame_num': self.frame_id,
'fish': new_frame_entries
})
if self.reverse:
self.frame_id -= 1
else:
self.frame_id += 1
def finalize(self, output_path=None, min_length=-1.0, min_travel=-1.0): # vert_margin=0.0
json_data = deepcopy(self.json_data)
# map (valid) fish IDs to 0, 1, 2, ...
fish_id_map = {}
for fish_id, count in self.fish_ids.items():
if count >= self.min_hits:
fish_id_map[fish_id] = len(fish_id_map)
# separate frame boxes into tracks, keyed by mapped IDs
# each track is a list of tuples ( bbox, frame_num )
tracks = { v : [] for _, v in fish_id_map.items() }
for frame in json_data['frames']:
for bbox in frame['fish']:
# check if valid
if bbox['fish_id'] in fish_id_map.keys():
track_id = fish_id_map[bbox['fish_id']]
tracks[track_id].append((bbox['bbox'], frame['frame_num']))
# map IDs and keep frame['fish'] sorted by ID
for i, frame in enumerate(json_data['frames']):
new_frame_entries = []
for frame_entry in frame['fish']:
if frame_entry['fish_id'] in fish_id_map:
frame_entry['fish_id'] = fish_id_map[frame_entry['fish_id']]
new_frame_entries.append(frame_entry)
frame['fish'] = sorted(new_frame_entries, key=lambda k: k['fish_id'])
# create summary 'fish' entry for json data
json_data['fish'] = []
for track_id, boxes in tracks.items():
fish_entry = {}
fish_entry['id'] = track_id
fish_entry['length'] = -1
# top = False
# bottom = False
# for frame in json_data['frames']:
# for frame_entry in frame['fish']:
# if frame_entry['fish_id'] == track_id:
# if frame_entry['bbox'][3] > vert_margin:
# top = True
# if frame_entry['bbox'][1] < 1 - vert_margin:
# bottom = True
# break
# if not top or not bottom:
# continue
start_bbox = boxes[0][0]
end_bbox = boxes[-1][0]
fish_entry['direction'] = Tracker.get_direction(start_bbox, end_bbox)
fish_entry['travel_dist'] = Tracker.get_travel_distance(start_bbox, end_bbox, json_data['image_meter_width'], json_data['image_meter_height'])
fish_entry['start_frame_index'] = boxes[0][1]
fish_entry['end_frame_index'] = boxes[-1][1]
fish_entry['color'] = Tracker.selectColor(track_id)
json_data['fish'].append(fish_entry)
# filter 'fish' field by fish length and travel distance
json_data = Fish_Length.add_lengths(json_data)
invalid_ids = []
if min_length != -1.0:
new_fish = []
for fish in json_data['fish']:
if fish['length'] > min_length and fish['travel_dist'] > min_travel:
new_fish.append(fish)
else:
invalid_ids.append(fish['id'])
json_data['fish'] = new_fish
# filter 'frames' field by fish length
if len(invalid_ids):
for frame in json_data['frames']:
new_fish = []
for fish in frame['fish']:
if fish['fish_id'] not in invalid_ids:
new_fish.append(fish)
frame['fish'] = new_fish
if output_path is not None:
with open(output_path,'w') as output:
json.dump(json_data, output, indent=2)
return json_data
def state(self, output_path=None):
json_data = deepcopy(self.json_data)
if output_path is not None:
with open(output_path,'w') as output:
json.dump(json_data, output, indent=2)
return json_data
@staticmethod
def selectColor(number):
hue = ((number * 137.508 + 60) % 360) / 360
return '#{0:02x}{1:02x}{2:02x}'.format(*(int(n * 255) for n in hls_to_rgb(hue, 0.5, 0.75)))
@staticmethod
def get_direction(start_bbox, end_bbox):
start_center = (start_bbox[2] + start_bbox[0])/2
end_center = (end_bbox[2] + end_bbox[0])/2
if start_center < 0.5 and end_center >= 0.5:
return 'right'
elif start_center >= 0.5 and end_center < 0.5:
return 'left'
else:
return 'none'
@staticmethod
def get_travel_distance(start_bbox, end_bbox, image_meter_width, image_meter_height):
dx = (start_bbox[2] + start_bbox[0])/2 - (end_bbox[2] + end_bbox[0])/2
dx *= image_meter_width
dy = (start_bbox[3] + start_bbox[1])/2 - (end_bbox[3] + end_bbox[1])/2
dy *= image_meter_height
return np.sqrt(dx*dx + dy*dy)
@staticmethod
def count_dirs(json_data):
right = 0
left = 0
none = 0
for fish_entry in json_data['fish']:
if fish_entry['direction'] == 'right':
right += 1
elif fish_entry['direction'] == 'left':
left += 1
else:
none += 1
return (right, left, none)
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