def __init__(self, input_type, input):
self.input_type = input_type
self.item_counter = 0
if input_type == 'json':
coco_annotation = COCO(input)
annotation_directory = os.path.join(os.getcwd(), os.path.dirname(input))
classes = ObjectDetectorJson.get_classes_from_coco_annotation(input)
self.json_data = ObjectDetectorJson.convert_coco_to_toolbox_format(coco_annotation, classes,
annotation_directory)
if input_type == "video":
self.cap = cv2.VideoCapture(input)
if input_type == "cam":
self.cap = cv2.VideoCapture(0)
def calc_coco_metrics(coco_annotations, predictions, classes):
annotations = ObjectDetectorJson.convert_coco_to_toolbox_format(
coco_annotations, classes)
detections = []
for annotation, prediction in zip(annotations, predictions):
width, height = annotation['image_size']
image_id = annotation['image_id']
for obj_id, obj in enumerate(prediction):
label = int(obj[1])
score = float(obj[2])
if obj_id != 0 and score == 0: # At least one prediction must be (COCO API issue)
continue
bbox = (obj[3:]).tolist()
bbox[::2] = [width * i for i in bbox[::2]]
bbox[1::2] = [height * i for i in bbox[1::2]]
xmin, ymin, xmax, ymax = bbox
w_bbox = round(xmax - xmin, 1)
h_bbox = round(ymax - ymin, 1)
xmin, ymin = round(xmin, 1), round(ymin, 1)
coco_det = {}
coco_det['image_id'] = image_id
coco_det['category_id'] = label
coco_det['bbox'] = [xmin, ymin, w_bbox, h_bbox]
coco_det['score'] = score
detections.append(coco_det)
coco_dt = coco_annotations.loadRes(detections)
img_ids = sorted(coco_annotations.getImgIds())
coco_eval = COCOeval(coco_annotations, coco_dt, 'bbox')
coco_eval.params.imgIds = img_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
metrics = {}
for metric_name, value in zip(METRICS_NAMES, coco_eval.stats):
metrics[metric_name] = value
return metrics
def calc_coco_metrics(coco_annotations, predictions, classes):
annotations = ObjectDetectorJson.convert_coco_to_toolbox_format(coco_annotations, classes)
detections = []
for annotation, prediction in zip(annotations, predictions):
width, height = annotation['image_size']
image_id = annotation['image_id']
for obj_id, obj in enumerate(prediction):
label = int(obj[1])
score = float(obj[2])
if obj_id != 0 and score == 0: # At least one prediction must be (COCO API issue)
continue
bbox = (obj[3:]).tolist()
bbox[::2] = [width * i for i in bbox[::2]]
bbox[1::2] = [height * i for i in bbox[1::2]]
xmin, ymin, xmax, ymax = bbox
w_bbox = round(xmax - xmin, 1)
h_bbox = round(ymax - ymin, 1)
xmin, ymin = round(xmin, 1), round(ymin, 1)
coco_det = {}
coco_det['image_id'] = image_id
coco_det['category_id'] = label
coco_det['bbox'] = [xmin, ymin, w_bbox, h_bbox]
coco_det['score'] = score
detections.append(coco_det)
coco_dt = coco_annotations.loadRes(detections)
img_ids = sorted(coco_annotations.getImgIds())
coco_eval = COCOeval(coco_annotations, coco_dt, 'bbox')
coco_eval.params.imgIds = img_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
metrics = {}
for metric_name, value in zip(METRICS_NAMES, coco_eval.stats):
metrics[metric_name] = value
return metrics
