def week2_adaptive_hsv(video: Video, debug=False) -> Iterator[Frame]:
model_mean, model_std = get_background_model(video,
int(2141 * 0.25),
total_frames=int(2141 * 0.25),
pixel_value=PixelValue.HSV)
ground_truth = read_detections(
'../datasets/AICity_data/train/S03/c010/gt/gt.txt')
frame_id = int(2141 * 0.25)
roi = cv2.cvtColor(
cv2.imread('../datasets/AICity_data/train/S03/c010/roi.jpg'),
cv2.COLOR_BGR2GRAY)
for im, mask in gaussian_model_adaptive(video,
int(2141 * 0.25),
model_mean,
model_std,
total_frames=int(2141 * 0.10),
pixel_value=PixelValue.HSV,
alpha=1.75,
rho=0.01):
mask = mask & roi
if debug:
cv2.imshow('f', mask)
cv2.waitKey()
mask = opening(mask, 7)
if debug:
cv2.imshow('f', mask)
cv2.waitKey()
mask = closing(mask, 35)
if debug:
cv2.imshow('f', mask)
cv2.waitKey()
mask, detections = find_boxes(mask)
frame = Frame(frame_id)
frame.detections = detections
frame.ground_truth = ground_truth[frame_id]
if debug:
mask2 = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
for detection in detections:
cv2.rectangle(
mask2,
(int(detection.top_left[1]), int(detection.top_left[0])),
(int(detection.get_bottom_right()[1]),
int(detection.get_bottom_right()[0])), (0, 255, 0), 5)
for gt in ground_truth[frame_id]:
cv2.rectangle(mask2,
(int(gt.top_left[1]), int(gt.top_left[0])),
(int(gt.get_bottom_right()[1]),
int(gt.get_bottom_right()[0])), (255, 0, 0), 5)
cv2.imshow('f', mask2)
cv2.waitKey()
yield im, mask, frame
frame_id += 1
def compute_map_gt_det(det_file, gt_file):
det_list = read_detections(det_file)
gt_list = read_detections(gt_file)
frames = []
for i in range(0, len(det_list)):
frame = Frame(i)
frame.detections = det_list[i]
frame.ground_truth = gt_list[i]
frames.append(frame)
mAP = mean_average_precision(frames, ignore_classes=True)
return mAP
def main():
start_frame = 1440
end_frame = 1789
gt = read_annotations('../annotations', start_frame, end_frame)
alg = 'mask_rcnn'
detections = read_detections('../datasets/AICity_data/train/S03/c010/det/det_{0}.txt'.format(alg))
kalman = KalmanTracking()
for i in range(start_frame, end_frame):
f = Frame(i)
f.detections = detections[i]
f.ground_truth = gt[i - start_frame]
kalman(f)
print(seq(f.detections).map(lambda d: d.id).to_list())
def main():
im_1440 = cv2.imread(
"../datasets/AICity_data_S03_c010_1440/frame_1440.jpg")
top_left = [995, 410]
width = 1241 - 995
height = 605 - 410
ground_truth = [Detection('', 'car', top_left, width, height)]
"""
DETECTIONS FROM ALTERED GROUND TRUTH
"""
frame = Frame(0, ground_truth)
frame.detections = alter_detections(ground_truth)
plot_frame(im_1440, frame)
iou = frame.get_detection_iou()
iou_mean = frame.get_detection_iou_mean()
print("IOU: ", iou, "IOU mean", iou_mean)
def week2_soa(video: Video, debug=False) -> Iterator[Frame]:
th = 150
frame_id = 0
fgbg = cv.createBackgroundSubtractorMOG2()
ground_truth = read_detections(
'../datasets/AICity_data/train/S03/c010/gt/gt.txt')
roi = cv.cvtColor(
cv.imread('../datasets/AICity_data/train/S03/c010/roi.jpg'),
cv.COLOR_BGR2GRAY)
for im in tqdm(video.get_frames(),
total=2141,
file=sys.stdout,
desc='Training model...'):
mask = fgbg.apply(im)
mask[mask < th] = 0
mask.astype(np.uint8) * 255
mask = mask & roi
mask = opening(mask, 5)
# cv.imshow('f', mask)
# cv.waitKey()
mask = closing(mask, 25)
# cv.imshow('f', mask)
# cv.waitKey()
mask, detections = find_boxes(mask)
frame = Frame(frame_id)
frame.detections = detections
frame.ground_truth = ground_truth[frame_id]
frame_id += 1
yield im, mask, frame
