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from dataloader import create_dataloader_aris
from aris import BEAM_WIDTH_DIR
import json
import cv2
import base64
VIDEO_HEIGHT = 700
def init_frames(video, preds, gp=None):
"""Load frames for annotation editing
Returns:
list({
frame: frame image as base64 string,
annotations: list(
bbox: dict of int defining bounding box {left, right, top, bottom},
id: id of fish as int,
conf: confidence in bbox as float
)
})
"""
if gp: gp(0, "Loading Frames")
dataloader, dataset = create_dataloader_aris(video, BEAM_WIDTH_DIR, None)
images = dataset.didson.load_frames(start_frame=0, end_frame=1)
# assumes all frames the same size
h, w = images[0].shape
# enforce a standard size so that text/box thickness is consistent
scale_factor = VIDEO_HEIGHT / h
h = VIDEO_HEIGHT
w = int(scale_factor*w)
annotations = []
if gp: gp(0, "Extracting Frames")
if len(preds['frames']):
for i, frame_info in enumerate(preds['frames']):
if gp: gp(i/len(preds['frames']), "Extracting Frames")
# Extract frames
img_raw = dataset.didson.load_frames(start_frame=i, end_frame=i+1)[0]
image = cv2.resize(cv2.cvtColor(img_raw, cv2.COLOR_GRAY2BGR), (w, h))
cv2.imwrite("annotation_frame_dir/" + str(i) + ".jpg", image)
# Extract annotations
frame = {
'annotations': [],
}
for fish in frame_info['fish']:
xmin, ymin, xmax, ymax = fish['bbox']
frame['annotations'].append({
'bbox': {
'left': int(round(xmin * w)),
'right': int(round(xmax * w)),
'top': int(round(ymin * h)),
'bottom': int(round(ymax * h)),
},
'id': str(fish['fish_id']),
'conf': fish['conf']
})
annotations.append(frame)
return annotations
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