Question: Is there a way to generate coco.json for a new directory of images.

[davodogster]

Hi, I need to be able to load the pre-trained model and apply it to unseen images (not train or val images), and then output the coco.json annotations file (and also image masks.png would be good). How do I do this? I can do this for validation images using --output_coco_json but I need to do it on test/new images.

Regards, Sam

[sainatarajan]

@davodogster

For this task, you need to write a post-processing script that solves your requirement. In order to get the annotations on the unseen images, do the following:

1. Modify the eval.py so that you get the contour points for each segmented object in an image.
2. Once you get the contour points, obtain the classes for them.
3. Create a python dictionary that follows the coco annotation format (there should be 4-5 keys).
4. Iterate over each segmented object for each image and then append the corresponding contour points to the dictionary.

[areebsyed]

@davodogster can you give me an idea about your first point? I want to extract the contour points of the detected/masked objects. I cant seem to figure out how. I know that the mask info is loaded as a list of tensors.

[davodogster]

@areebsyed @sainatarajan Hi guys I've modified eval.py to produce binary instance masks for every instance (taking the largest contour when there are two masks inside one box), for every image. Then I wrote a script to rename all the images and masks so that it can finally be passed in the exact format to waspinators pycococreator script (hardcode iscrowd = 0) to generate the coco.json from the masks.

I modified the functions prep_display, evalimages, and evalimage.

` from data import COCODetection, get_label_map, MEANS, COLORS from yolact import Yolact from utils.augmentations import BaseTransform, FastBaseTransform, Resize from utils.functions import MovingAverage, ProgressBar from layers.box_utils import jaccard, center_size, mask_iou from utils import timer from utils.functions import SavePath from layers.output_utils import postprocess, undo_image_transformation import pycocotools

from data import cfg, set_cfg, set_dataset

import numpy as np import torch device = torch.device("cuda:0") import torch.backends.cudnn as cudnn from torch.autograd import Variable import argparse import time import random import cProfile import pickle import json import os from collections import defaultdict from pathlib import Path from collections import OrderedDict from PIL import Image

import matplotlib.pyplot as plt import cv2

def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.')

def parse_args(argv=None): parser = argparse.ArgumentParser( description='YOLACT COCO Evaluation') parser.add_argument('--trained_model', default='weights/ssd300_mAP_77.43_v2.pth', type=str, help='Trained state_dict file path to open. If "interrupt", this will open the interrupt file.') parser.add_argument('--top_k', default=5, type=int, help='Further restrict the number of predictions to parse') parser.add_argument('--cuda', default=True, type=str2bool, help='Use cuda to evaulate model') parser.add_argument('--fast_nms', default=True, type=str2bool, help='Whether to use a faster, but not entirely correct version of NMS.') parser.add_argument('--cross_class_nms', default=False, type=str2bool, help='Whether compute NMS cross-class or per-class.') parser.add_argument('--display_masks', default=True, type=str2bool, help='Whether or not to display masks over bounding boxes') parser.add_argument('--display_bboxes', default=True, type=str2bool, help='Whether or not to display bboxes around masks') parser.add_argument('--display_text', default=True, type=str2bool, help='Whether or not to display text (class [score])') parser.add_argument('--display_scores', default=True, type=str2bool, help='Whether or not to display scores in addition to classes') parser.add_argument('--display', dest='display', action='store_true', help='Display qualitative results instead of quantitative ones.') parser.add_argument('--shuffle', dest='shuffle', action='store_true', help='Shuffles the images when displaying them. Doesn\'t have much of an effect when display is off though.') parser.add_argument('--ap_data_file', default='results/ap_data.pkl', type=str, help='In quantitative mode, the file to save detections before calculating mAP.') parser.add_argument('--resume', dest='resume', action='store_true', help='If display not set, this resumes mAP calculations from the ap_data_file.') parser.add_argument('--max_images', default=-1, type=int, help='The maximum number of images from the dataset to consider. Use -1 for all.') parser.add_argument('--output_coco_json', dest='output_coco_json', action='store_true', help='If display is not set, instead of processing IoU values, this just dumps detections into the coco json file.') parser.add_argument('--bbox_det_file', default='results/bbox_detections.json', type=str, help='The output file for coco bbox results if --coco_results is set.') parser.add_argument('--mask_det_file', default='results/mask_detections.json', type=str, help='The output file for coco mask results if --coco_results is set.') parser.add_argument('--config', default=None, help='The config object to use.') parser.add_argument('--output_web_json', dest='output_web_json', action='store_true', help='If display is not set, instead of processing IoU values, this dumps detections for usage with the detections viewer web thingy.') parser.add_argument('--web_det_path', default='web/dets/', type=str, help='If output_web_json is set, this is the path to dump detections into.') parser.add_argument('--no_bar', dest='no_bar', action='store_true', help='Do not output the status bar. This is useful for when piping to a file.') parser.add_argument('--display_lincomb', default=False, type=str2bool, help='If the config uses lincomb masks, output a visualization of how those masks are created.') parser.add_argument('--benchmark', default=False, dest='benchmark', action='store_true', help='Equivalent to running display mode but without displaying an image.') parser.add_argument('--no_sort', default=False, dest='no_sort', action='store_true', help='Do not sort images by hashed image ID.') parser.add_argument('--seed', default=None, type=int, help='The seed to pass into random.seed. Note: this is only really for the shuffle and does not (I think) affect cuda stuff.') parser.add_argument('--mask_proto_debug', default=False, dest='mask_proto_debug', action='store_true', help='Outputs stuff for scripts/compute_mask.py.') parser.add_argument('--no_crop', default=False, dest='crop', action='store_false', help='Do not crop output masks with the predicted bounding box.') parser.add_argument('--image', default=None, type=str, help='A path to an image to use for display.') parser.add_argument('--images', default=None, type=str, help='An input folder of images and output folder to save detected images. Should be in the format input->output.') parser.add_argument('--video', default=None, type=str, help='A path to a video to evaluate on. Passing in a number will use that index webcam.') parser.add_argument('--video_multiframe', default=1, type=int, help='The number of frames to evaluate in parallel to make videos play at higher fps.') parser.add_argument('--score_threshold', default=0, type=float, help='Detections with a score under this threshold will not be considered. This currently only works in display mode.') parser.add_argument('--dataset', default=None, type=str, help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).') parser.add_argument('--detect', default=False, dest='detect', action='store_true', help='Don\'t evauluate the mask branch at all and only do object detection. This only works for --display and --benchmark.') parser.add_argument('--display_fps', default=False, dest='display_fps', action='store_true', help='When displaying / saving video, draw the FPS on the frame') parser.add_argument('--emulate_playback', default=False, dest='emulate_playback', action='store_true', help='When saving a video, emulate the framerate that you\'d get running in real-time mode.')

parser.set_defaults(no_bar=False, display=False, resume=False, output_coco_json=False, output_web_json=False,
                    shuffle=False,
                    benchmark=False, no_sort=False, no_hash=False, mask_proto_debug=False, crop=True, detect=False,
                    display_fps=False,
                    emulate_playback=False)

global args
args = parser.parse_args(argv)

if args.output_web_json:
    args.output_coco_json = True

if args.seed is not None:
    random.seed(args.seed)

iou_thresholds = [x / 100 for x in range(50, 100, 5)] coco_cats = {} # Call prep_coco_cats to fill this coco_cats_inv = {} color_cache = defaultdict(lambda: {})

def prep_display(detsout, img, path, out_dir, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''): """ Note: If undo_transform=False then im_h and im_w are allowed to be None. """ pathsplit = Path(path).parts

print(path_split)

path2 = path_split[1]
print("path", path2)
print("out_dir", out_dir)
if undo_transform:
    img_numpy = undo_image_transformation(img, w, h)
    img_gpu = torch.Tensor(img_numpy).cuda()
else:
    img_gpu = img / 255.0
    h, w, _ = img.shape

with timer.env('Postprocess'):
    save = cfg.rescore_bbox
    cfg.rescore_bbox = True
    t = postprocess(dets_out, w, h, visualize_lincomb=args.display_lincomb,
                    crop_masks=args.crop,
                    score_threshold=args.score_threshold)
    cfg.rescore_bbox = save

with timer.env('Copy'):
    idx = t[1].argsort(0, descending=True)[:args.top_k]

    if cfg.eval_mask_branch:
        # Masks are drawn on the GPU, so don't copy
        masks = t[3][idx]
    classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]

num_dets_to_consider = min(args.top_k, classes.shape[0])
for j in range(num_dets_to_consider):
    if scores[j] < args.score_threshold:
        num_dets_to_consider = j
        break

# Quick and dirty lambda for selecting the color for a particular index
# Also keeps track of a per-gpu color cache for maximum speed
def get_color(j, on_gpu=None):
    global color_cache
    color_idx = (classes[j] * 50 if class_color else j * 100) % len(COLORS)

    if on_gpu is not None and color_idx in color_cache[on_gpu]:
        return color_cache[on_gpu][color_idx]
    else:
        color = COLORS[color_idx]
        if not undo_transform:
            # The image might come in as RGB or BRG, depending
            color = (random.randint(0, 255), random.randint(0, 255),
                     random.randint(0, 255))  # (7, 0, 255) #(color[2], color[1], color[0]) # (255, 0, 7)
        if on_gpu is not None:
            color = torch.Tensor(color).to(on_gpu).float() / 255.
            color_cache[on_gpu][color_idx] = color
        return color

# First, draw the masks on the GPU where we can do it really fast
# Beware: very fast but possibly unintelligible mask-drawing code ahead
# I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
    masks = masks[:num_dets_to_consider, :, :, None]
    #print('maskshape', (masks.shape))
    for i in range(num_dets_to_consider):
        msk = masks[i, :, :, None]
        #print("shape", msk.shape)
        mask = msk.view(1, msk.shape[0], msk.shape[1], 1)
        #print('newmaskshape', (mask.shape))
        img_gpu_masked = (mask.sum(dim=0) >= 1).float().expand(-1, -1, 3) # img_gpu *
        img_numpy = (img_gpu_masked * 255).byte().cpu().numpy()
        img_numpy = cv2.cvtColor(img_numpy, cv2.COLOR_RGB2GRAY)

        cnts, _ = cv2.findContours(img_numpy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnt = max(cnts, key=cv2.contourArea)
        out = np.zeros(img_numpy.shape, np.uint8)

        #out[idx[:,0,0],idx[:0,1]] = 255
        #img_numpy = cv2.cvtColor(img_numpy, cv2.COLOR_RGB2GRAY)
        #ret, thresh = cv2.threshold(img_numpy, 127, 255, 0)
        #inter = cv2.morphologyEx(img_numpy, cv2.MORPH_CLOSE)
        #cnts, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # RETR_TREE
        #cnt = max(cnts, key=cv2.contourArea)
        #print(cnt)
        #out = np.zeros(img_numpy.shape, np.float32) #np.uint8)
        # image_id = self.bbox_data["image_id"]

        cv2.drawContours(out, [cnt], -1, 255, cv2.FILLED)
        out = cv2.bitwise_and(img_numpy, out)
        #return (out)
        # img_gpu = torch.from_numpy(out).float().to(device)
        print(out_dir + '/' + path2 + '_mask' + str(i) + '.png') # '/home/davidsos/Documents/yolact_mussel28/' +
        # cv2.imwrite(out_dir + '/' + path_ + '_mask' + str(i) + '.png', out) # works for single image :) # '/home/davidsos/Documents/yolact_mussel28/'
        cv2.imwrite(out_dir + '/' + path2 + '_mask' + str(i) + '.png', out) # works for single image :) # '/home/davidsos/Documents/yolact_mussel28/'

if args.display_fps:
    # Draw the box for the fps on the GPU
    font_face = cv2.FONT_HERSHEY_DUPLEX
    font_scale = 0.6
    font_thickness = 1

    text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]

    img_gpu[0:text_h + 8, 0:text_w + 8] *= 0.6  # 1 - Box alpha

# Then draw the stuff that needs to be done on the cpu
# Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
img_numpy = (img_gpu * 255).byte().cpu().numpy()

if args.display_fps:
    # Draw the text on the CPU
    text_pt = (4, text_h + 2)
    text_color = [255, 255, 255]

    cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)

if num_dets_to_consider == 0:
    return img_numpy

if args.display_text or args.display_bboxes:
    for j in reversed(range(num_dets_to_consider)):
        x1, y1, x2, y2 = boxes[j, :]
        color = get_color(j)
        score = scores[j]

        if args.display_bboxes:
            cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)

        if args.display_text:
            _class = cfg.dataset.class_names[classes[j]]
            text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class

            font_face = cv2.FONT_HERSHEY_DUPLEX
            font_scale = 0.6
            font_thickness = 1

            text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]

            text_pt = (x1, y1 - 3)
            text_color = [255, 255, 255]

            cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
            cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness,
                        cv2.LINE_AA)

return img_numpy

def prep_benchmark(dets_out, h, w): with timer.env('Postprocess'): t = postprocess(dets_out, w, h, crop_masks=args.crop, score_threshold=args.score_threshold)

with timer.env('Copy'):
    classes, scores, boxes, masks = [x[:args.top_k] for x in t]
    if isinstance(scores, list):
        box_scores = scores[0].cpu().numpy()
        mask_scores = scores[1].cpu().numpy()
    else:
        scores = scores.cpu().numpy()
    classes = classes.cpu().numpy()
    boxes = boxes.cpu().numpy()
    masks = masks.cpu().numpy()

with timer.env('Sync'):
    # Just in case
    torch.cuda.synchronize()

def prep_coco_cats(): """ Prepare inverted table for category id lookup given a coco cats object. """ for coco_cat_id, transformed_cat_id_p1 in get_label_map().items(): transformed_cat_id = transformed_cat_id_p1 - 1 coco_cats[transformed_cat_id] = coco_cat_id coco_cats_inv[coco_cat_id] = transformed_cat_id

def get_coco_cat(transformed_cat_id): """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """ return coco_cats[transformed_cat_id]

def get_transformed_cat(coco_cat_id): """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """ return coco_cats_inv[coco_cat_id]

class Detections:

def __init__(self):
    self.bbox_data = []
    self.mask_data = []

def add_bbox(self, image_id: int, category_id: int, bbox: list, score: float):
    """ Note that bbox should be a list or tuple of (x1, y1, x2, y2) """
    bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]

    # Round to the nearest 10th to avoid huge file sizes, as COCO suggests
    bbox = [round(float(x) * 10) / 10 for x in bbox]

    self.bbox_data.append({
        'image_id': int(image_id),
        'category_id': get_coco_cat(int(category_id)),
        'bbox': bbox,
        'score': float(score)
    })

def add_mask(self, image_id: int, category_id: int, segmentation: np.ndarray, score: float):
    """ The segmentation should be the full mask, the size of the image and with size [h, w]. """
    rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8)))
    rle['counts'] = rle['counts'].decode('ascii')  # json.dump doesn't like bytes strings

    self.mask_data.append({
        'image_id': int(image_id),
        'category_id': get_coco_cat(int(category_id)),
        'segmentation': rle,
        'score': float(score)
    })

def dump(self):
    dump_arguments = [
        (self.bbox_data, args.bbox_det_file),
        (self.mask_data, args.mask_det_file)
    ]

    for data, path in dump_arguments:
        with open(path, 'w') as f:
            json.dump(data, f)

def dump_web(self):
    """ Dumps it in the format for my web app. Warning: bad code ahead! """
    config_outs = ['preserve_aspect_ratio', 'use_prediction_module',
                   'use_yolo_regressors', 'use_prediction_matching',
                   'train_masks']

    output = {
        'info': {
            'Config': {key: getattr(cfg, key) for key in config_outs},
        }
    }

    image_ids = list(set([x['image_id'] for x in self.bbox_data]))
    image_ids.sort()
    image_lookup = {_id: idx for idx, _id in enumerate(image_ids)}

    output['images'] = [{'image_id': image_id, 'dets': []} for image_id in image_ids]

    # These should already be sorted by score with the way prep_metrics works.
    for bbox, mask in zip(self.bbox_data, self.mask_data):
        image_obj = output['images'][image_lookup[bbox['image_id']]]
        image_obj['dets'].append({
            'score': bbox['score'],
            'bbox': bbox['bbox'],
            'category': cfg.dataset.class_names[get_transformed_cat(bbox['category_id'])],
            'mask': mask['segmentation'],
        })

    with open(os.path.join(args.web_det_path, '%s.json' % cfg.name), 'w') as f:
        json.dump(output, f)

def _mask_iou(mask1, mask2, iscrowd=False): with timer.env('Mask IoU'): ret = mask_iou(mask1, mask2, iscrowd) return ret.cpu()

def _bbox_iou(bbox1, bbox2, iscrowd=False): with timer.env('BBox IoU'): ret = jaccard(bbox1, bbox2, iscrowd) return ret.cpu()

def prep_metrics(ap_data, dets, img, gt, gt_masks, h, w, num_crowd, image_id, detections: Detections = None): """ Returns a list of APs for this image, with each element being for a class """ if not args.output_coco_json: with timer.env('Prepare gt'): gt_boxes = torch.Tensor(gt[:, :4]) gt_boxes[:, [0, 2]] = w gt_boxes[:, [1, 3]] = h gt_classes = list(gt[:, 4].astype(int)) gt_masks = torch.Tensor(gt_masks).view(-1, h * w)

        if num_crowd > 0:
            split = lambda x: (x[-num_crowd:], x[:-num_crowd])
            crowd_boxes, gt_boxes = split(gt_boxes)
            crowd_masks, gt_masks = split(gt_masks)
            crowd_classes, gt_classes = split(gt_classes)

with timer.env('Postprocess'):
    classes, scores, boxes, masks = postprocess(dets, w, h, crop_masks=args.crop,
                                                score_threshold=args.score_threshold)

    if classes.size(0) == 0:
        return

    classes = list(classes.cpu().numpy().astype(int))
    if isinstance(scores, list):
        box_scores = list(scores[0].cpu().numpy().astype(float))
        mask_scores = list(scores[1].cpu().numpy().astype(float))
    else:
        scores = list(scores.cpu().numpy().astype(float))
        box_scores = scores
        mask_scores = scores
    masks = masks.view(-1, h * w).cuda()
    boxes = boxes.cuda()

if args.output_coco_json:
    with timer.env('JSON Output'):
        boxes = boxes.cpu().numpy()
        masks = masks.view(-1, h, w).cpu().numpy()
        for i in range(masks.shape[0]):
            # Make sure that the bounding box actually makes sense and a mask was produced
            if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] - boxes[i, 0]) > 0:
                detections.add_bbox(image_id, classes[i], boxes[i, :], box_scores[i])
                detections.add_mask(image_id, classes[i], masks[i, :, :], mask_scores[i])
        return

with timer.env('Eval Setup'):
    num_pred = len(classes)
    num_gt = len(gt_classes)

    mask_iou_cache = _mask_iou(masks, gt_masks)
    bbox_iou_cache = _bbox_iou(boxes.float(), gt_boxes.float())

    if num_crowd > 0:
        crowd_mask_iou_cache = _mask_iou(masks, crowd_masks, iscrowd=True)
        crowd_bbox_iou_cache = _bbox_iou(boxes.float(), crowd_boxes.float(), iscrowd=True)
    else:
        crowd_mask_iou_cache = None
        crowd_bbox_iou_cache = None

    box_indices = sorted(range(num_pred), key=lambda i: -box_scores[i])
    mask_indices = sorted(box_indices, key=lambda i: -mask_scores[i])

    iou_types = [
        ('box', lambda i, j: bbox_iou_cache[i, j].item(),
         lambda i, j: crowd_bbox_iou_cache[i, j].item(),
         lambda i: box_scores[i], box_indices),
        ('mask', lambda i, j: mask_iou_cache[i, j].item(),
         lambda i, j: crowd_mask_iou_cache[i, j].item(),
         lambda i: mask_scores[i], mask_indices)
    ]

timer.start('Main loop')
for _class in set(classes + gt_classes):
    ap_per_iou = []
    num_gt_for_class = sum([1 for x in gt_classes if x == _class])

    for iouIdx in range(len(iou_thresholds)):
        iou_threshold = iou_thresholds[iouIdx]

        for iou_type, iou_func, crowd_func, score_func, indices in iou_types:
            gt_used = [False] * len(gt_classes)

            ap_obj = ap_data[iou_type][iouIdx][_class]
            ap_obj.add_gt_positives(num_gt_for_class)

            for i in indices:
                if classes[i] != _class:
                    continue

                max_iou_found = iou_threshold
                max_match_idx = -1
                for j in range(num_gt):
                    if gt_used[j] or gt_classes[j] != _class:
                        continue

                    iou = iou_func(i, j)

                    if iou > max_iou_found:
                        max_iou_found = iou
                        max_match_idx = j

                if max_match_idx >= 0:
                    gt_used[max_match_idx] = True
                    ap_obj.push(score_func(i), True)
                else:
                    # If the detection matches a crowd, we can just ignore it
                    matched_crowd = False

                    if num_crowd > 0:
                        for j in range(len(crowd_classes)):
                            if crowd_classes[j] != _class:
                                continue

                            iou = crowd_func(i, j)

                            if iou > iou_threshold:
                                matched_crowd = True
                                break

                    # All this crowd code so that we can make sure that our eval code gives the
                    # same result as COCOEval. There aren't even that many crowd annotations to
                    # begin with, but accuracy is of the utmost importance.
                    if not matched_crowd:
                        ap_obj.push(score_func(i), False)
timer.stop('Main loop')

class APDataObject: """ Stores all the information necessary to calculate the AP for one IoU and one class. Note: I type annotated this because why not. """

def __init__(self):
    self.data_points = []
    self.num_gt_positives = 0

def push(self, score: float, is_true: bool):
    self.data_points.append((score, is_true))

def add_gt_positives(self, num_positives: int):
    """ Call this once per image. """
    self.num_gt_positives += num_positives

def is_empty(self) -> bool:
    return len(self.data_points) == 0 and self.num_gt_positives == 0

def get_ap(self) -> float:
    """ Warning: result not cached. """

    if self.num_gt_positives == 0:
        return 0

    # Sort descending by score
    self.data_points.sort(key=lambda x: -x[0])

    precisions = []
    recalls = []
    num_true = 0
    num_false = 0

    # Compute the precision-recall curve. The x axis is recalls and the y axis precisions.
    for datum in self.data_points:
        # datum[1] is whether the detection a true or false positive
        if datum[1]:
            num_true += 1
        else:
            num_false += 1

        precision = num_true / (num_true + num_false)
        recall = num_true / self.num_gt_positives

        precisions.append(precision)
        recalls.append(recall)

    # Smooth the curve by computing [max(precisions[i:]) for i in range(len(precisions))]
    # Basically, remove any temporary dips from the curve.
    # At least that's what I think, idk. COCOEval did it so I do too.
    for i in range(len(precisions) - 1, 0, -1):
        if precisions[i] > precisions[i - 1]:
            precisions[i - 1] = precisions[i]

    # Compute the integral of precision(recall) d_recall from recall=0->1 using fixed-length riemann summation with 101 bars.
    y_range = [0] * 101  # idx 0 is recall == 0.0 and idx 100 is recall == 1.00
    x_range = np.array([x / 100 for x in range(101)])
    recalls = np.array(recalls)

    # I realize this is weird, but all it does is find the nearest precision(x) for a given x in x_range.
    # Basically, if the closest recall we have to 0.01 is 0.009 this sets precision(0.01) = precision(0.009).
    # I approximate the integral this way, because that's how COCOEval does it.
    indices = np.searchsorted(recalls, x_range, side='left')
    for bar_idx, precision_idx in enumerate(indices):
        if precision_idx < len(precisions):
            y_range[bar_idx] = precisions[precision_idx]

    # Finally compute the riemann sum to get our integral.
    # avg([precision(x) for x in 0:0.01:1])
    return sum(y_range) / len(y_range)

def badhash(x): """ Just a quick and dirty hash function for doing a deterministic shuffle based on image_id.

Source:
https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key
"""
x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF
x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF
x = ((x >> 16) ^ x) & 0xFFFFFFFF
return x

def evalimage(net: Yolact, path: str, save_path: str = None): print(path) out_dir = save_path frame = torch.from_numpy(cv2.imread(path)).cuda().float() batch = FastBaseTransform()(frame.unsqueeze(0)) preds = net(batch)

# prep display
# img_numpy = prep_display(preds, frame, path, None, None, undo_transform=False)
prep_display(preds, frame, path, out_dir, None, None, undo_transform=False)

#cv2.imwrite('/home/davidsos/Documents/yolact_mussel28/mask_output_multi/' + path + '_mask' + str(i) + '.png', img_numpy) # works for single image :)

# if save_path is None:
#     img_numpy = img_numpy[:, :, (2, 1, 0)]
#
# if save_path is None:
#     plt.imshow(img_numpy)
#     plt.title(path)
#     plt.show()
# else:
#     cv2.imwrite(save_path, img_numpy)

def evalimages(net: Yolact, input_folder: str, output_folder: str): if not os.path.exists(output_folder): os.mkdir(output_folder)

# print()
# loop over test images in input folder
for parth in Path(input_folder).glob('*'):
    image_path = str(parth)
    #name = os.path.basename(path)
    #name = '.'.join(name.split('.')[:-1]) + '.png'
    # out_path = os.path.join(output_folder, name)
    ## loop within the image ??

    # eval_image calls prep_display and writes the masks for an image
    evalimage(net, image_path, output_folder)
    # print(parth + ' -> ' + output_folder)
print('Done.')

from multiprocessing.pool import ThreadPool from queue import Queue

class CustomDataParallel(torch.nn.DataParallel): """ A Custom Data Parallel class that properly gathers lists of dictionaries. """

def gather(self, outputs, output_device):
    # Note that I don't actually want to convert everything to the output_device
    return sum(outputs, [])

def evalvideo(net: Yolact, path: str, out_path: str = None):

If the path is a digit, parse it as a webcam index

is_webcam = path.isdigit()

# If the input image size is constant, this make things faster (hence why we can use it in a video setting).
cudnn.benchmark = True

if is_webcam:
    vid = cv2.VideoCapture(int(path))
else:
    vid = cv2.VideoCapture(path)

if not vid.isOpened():
    print('Could not open video "%s"' % path)
    exit(-1)

target_fps = round(vid.get(cv2.CAP_PROP_FPS))
frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))

if is_webcam:
    num_frames = float('inf')
else:
    num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))

net = CustomDataParallel(net).cuda()
transform = torch.nn.DataParallel(FastBaseTransform()).cuda()
frame_times = MovingAverage(100)
fps = 0
frame_time_target = 1 / target_fps
running = True
fps_str = ''
vid_done = False
frames_displayed = 0

if out_path is not None:
    out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (frame_width, frame_height))

def cleanup_and_exit():
    print()
    pool.terminate()
    vid.release()
    if out_path is not None:
        out.release()
    cv2.destroyAllWindows()
    exit()

def get_next_frame(vid):
    frames = []
    for idx in range(args.video_multiframe):
        frame = vid.read()[1]
        if frame is None:
            return frames
        frames.append(frame)
    return frames

def transform_frame(frames):
    with torch.no_grad():
        frames = [torch.from_numpy(frame).cuda().float() for frame in frames]
        return frames, transform(torch.stack(frames, 0))

def eval_network(inp):
    with torch.no_grad():
        frames, imgs = inp
        num_extra = 0
        while imgs.size(0) < args.video_multiframe:
            imgs = torch.cat([imgs, imgs[0].unsqueeze(0)], dim=0)
            num_extra += 1
        out = net(imgs)
        if num_extra > 0:
            out = out[:-num_extra]
        return frames, out

def prep_frame(inp, fps_str):
    with torch.no_grad():
        frame, preds = inp
        return prep_display(preds, frame, None, None, undo_transform=False, class_color=True, fps_str=fps_str)

frame_buffer = Queue()
video_fps = 0

# All this timing code to make sure that
def play_video():
    try:
        nonlocal frame_buffer, running, video_fps, is_webcam, num_frames, frames_displayed, vid_done

        video_frame_times = MovingAverage(100)
        frame_time_stabilizer = frame_time_target
        last_time = None
        stabilizer_step = 0.0005
        progress_bar = ProgressBar(30, num_frames)

        while running:
            frame_time_start = time.time()

            if not frame_buffer.empty():
                next_time = time.time()
                if last_time is not None:
                    video_frame_times.add(next_time - last_time)
                    video_fps = 1 / video_frame_times.get_avg()
                if out_path is None:
                    cv2.imshow(path, frame_buffer.get())
                else:
                    out.write(frame_buffer.get())
                frames_displayed += 1
                last_time = next_time

                if out_path is not None:
                    if video_frame_times.get_avg() == 0:
                        fps = 0
                    else:
                        fps = 1 / video_frame_times.get_avg()
                    progress = frames_displayed / num_frames * 100
                    progress_bar.set_val(frames_displayed)

                    print('\rProcessing Frames  %s %6d / %6d (%5.2f%%)    %5.2f fps        '
                          % (repr(progress_bar), frames_displayed, num_frames, progress, fps), end='')

            # This is split because you don't want savevideo to require cv2 display functionality (see #197)
            if out_path is None and cv2.waitKey(1) == 27:
                # Press Escape to close
                running = False
            if not (frames_displayed < num_frames):
                running = False

            if not vid_done:
                buffer_size = frame_buffer.qsize()
                if buffer_size < args.video_multiframe:
                    frame_time_stabilizer += stabilizer_step
                elif buffer_size > args.video_multiframe:
                    frame_time_stabilizer -= stabilizer_step
                    if frame_time_stabilizer < 0:
                        frame_time_stabilizer = 0

                new_target = frame_time_stabilizer if is_webcam else max(frame_time_stabilizer, frame_time_target)
            else:
                new_target = frame_time_target

            next_frame_target = max(2 * new_target - video_frame_times.get_avg(), 0)
            target_time = frame_time_start + next_frame_target - 0.001  # Let's just subtract a millisecond to be safe

            if out_path is None or args.emulate_playback:
                # This gives more accurate timing than if sleeping the whole amount at once
                while time.time() < target_time:
                    time.sleep(0.001)
            else:
                # Let's not starve the main thread, now
                time.sleep(0.001)
    except:
        # See issue #197 for why this is necessary
        import traceback
        traceback.print_exc()

extract_frame = lambda x, i: (
x[0][i] if x[1][i]['detection'] is None else x[0][i].to(x[1][i]['detection']['box'].device), [x[1][i]])

# Prime the network on the first frame because I do some thread unsafe things otherwise
print('Initializing model... ', end='')
first_batch = eval_network(transform_frame(get_next_frame(vid)))
print('Done.')

# For each frame the sequence of functions it needs to go through to be processed (in reversed order)
sequence = [prep_frame, eval_network, transform_frame]
pool = ThreadPool(processes=len(sequence) + args.video_multiframe + 2)
pool.apply_async(play_video)
active_frames = [{'value': extract_frame(first_batch, i), 'idx': 0} for i in range(len(first_batch[0]))]

print()
if out_path is None: print('Press Escape to close.')
try:
    while vid.isOpened() and running:
        # Hard limit on frames in buffer so we don't run out of memory >.>
        while frame_buffer.qsize() > 100:
            time.sleep(0.001)

        start_time = time.time()

        # Start loading the next frames from the disk
        if not vid_done:
            next_frames = pool.apply_async(get_next_frame, args=(vid,))
        else:
            next_frames = None

        if not (vid_done and len(active_frames) == 0):
            # For each frame in our active processing queue, dispatch a job
            # for that frame using the current function in the sequence
            for frame in active_frames:
                _args = [frame['value']]
                if frame['idx'] == 0:
                    _args.append(fps_str)
                frame['value'] = pool.apply_async(sequence[frame['idx']], args=_args)

            # For each frame whose job was the last in the sequence (i.e. for all final outputs)
            for frame in active_frames:
                if frame['idx'] == 0:
                    frame_buffer.put(frame['value'].get())

            # Remove the finished frames from the processing queue
            active_frames = [x for x in active_frames if x['idx'] > 0]

            # Finish evaluating every frame in the processing queue and advanced their position in the sequence
            for frame in list(reversed(active_frames)):
                frame['value'] = frame['value'].get()
                frame['idx'] -= 1

                if frame['idx'] == 0:
                    # Split this up into individual threads for prep_frame since it doesn't support batch size
                    active_frames += [{'value': extract_frame(frame['value'], i), 'idx': 0} for i in
                                      range(1, len(frame['value'][0]))]
                    frame['value'] = extract_frame(frame['value'], 0)

            # Finish loading in the next frames and add them to the processing queue
            if next_frames is not None:
                frames = next_frames.get()
                if len(frames) == 0:
                    vid_done = True
                else:
                    active_frames.append({'value': frames, 'idx': len(sequence) - 1})

            # Compute FPS
            frame_times.add(time.time() - start_time)
            fps = args.video_multiframe / frame_times.get_avg()
        else:
            fps = 0

        fps_str = 'Processing FPS: %.2f | Video Playback FPS: %.2f | Frames in Buffer: %d' % (
        fps, video_fps, frame_buffer.qsize())
        if not args.display_fps:
            print('\r' + fps_str + '    ', end='')

except KeyboardInterrupt:
    print('\nStopping...')

cleanup_and_exit()

def evaluate(net: Yolact, dataset, train_mode=False): net.detect.use_fast_nms = args.fast_nms net.detect.use_cross_class_nms = args.cross_class_nms cfg.mask_proto_debug = args.mask_proto_debug

# TODO Currently we do not support Fast Mask Re-scroing in evalimage, evalimages, and evalvideo
if args.image is not None:
    if ':' in args.image:
        inp, out = args.image.split(':')
        evalimage(net, inp, out)
    else:
        evalimage(net, args.image)
    return
elif args.images is not None:
    inp, out = args.images.split(':')
    evalimages(net, inp, out)
    return
elif args.video is not None:
    if ':' in args.video:
        inp, out = args.video.split(':')
        evalvideo(net, inp, out)
    else:
        evalvideo(net, args.video)
    return

frame_times = MovingAverage()
dataset_size = len(dataset) if args.max_images < 0 else min(args.max_images, len(dataset))
progress_bar = ProgressBar(30, dataset_size)

print()

if not args.display and not args.benchmark:
    # For each class and iou, stores tuples (score, isPositive)
    # Index ap_data[type][iouIdx][classIdx]
    ap_data = {
        'box': [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds],
        'mask': [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds]
    }
    detections = Detections()
else:
    timer.disable('Load Data')

dataset_indices = list(range(len(dataset)))

if args.shuffle:
    random.shuffle(dataset_indices)
elif not args.no_sort:
    # Do a deterministic shuffle based on the image ids
    #
    # I do this because on python 3.5 dictionary key order is *random*, while in 3.6 it's
    # the order of insertion. That means on python 3.6, the images come in the order they are in
    # in the annotations file. For some reason, the first images in the annotations file are
    # the hardest. To combat this, I use a hard-coded hash function based on the image ids
    # to shuffle the indices we use. That way, no matter what python version or how pycocotools
    # handles the data, we get the same result every time.
    hashed = [badhash(x) for x in dataset.ids]
    dataset_indices.sort(key=lambda x: hashed[x])

dataset_indices = dataset_indices[:dataset_size]

try:
    # Main eval loop
    for it, image_idx in enumerate(dataset_indices):
        timer.reset()

        with timer.env('Load Data'):
            img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)

            # Test flag, do not upvote
            if cfg.mask_proto_debug:
                with open('scripts/info.txt', 'w') as f:
                    f.write(str(dataset.ids[image_idx]))
                np.save('scripts/gt.npy', gt_masks)

            batch = Variable(img.unsqueeze(0))
            if args.cuda:
                batch = batch.cuda()

        with timer.env('Network Extra'):
            preds = net(batch)
        # Perform the meat of the operation here depending on our mode.
        if args.display:
            img_numpy = prep_display(preds, img, h, w)
        elif args.benchmark:
            prep_benchmark(preds, h, w)
        else:
            prep_metrics(ap_data, preds, img, gt, gt_masks, h, w, num_crowd, dataset.ids[image_idx], detections)

        # First couple of images take longer because we're constructing the graph.
        # Since that's technically initialization, don't include those in the FPS calculations.
        if it > 1:
            frame_times.add(timer.total_time())

        if args.display:
            if it > 1:
                print('Avg FPS: %.4f' % (1 / frame_times.get_avg()))
            plt.imshow(img_numpy)
            plt.title(str(dataset.ids[image_idx]))
            plt.show()
        elif not args.no_bar:
            if it > 1:
                fps = 1 / frame_times.get_avg()
            else:
                fps = 0
            progress = (it + 1) / dataset_size * 100
            progress_bar.set_val(it + 1)
            print('\rProcessing Images  %s %6d / %6d (%5.2f%%)    %5.2f fps        '
                  % (repr(progress_bar), it + 1, dataset_size, progress, fps), end='')

    if not args.display and not args.benchmark:
        print()
        if args.output_coco_json:
            print('Dumping detections...')
            if args.output_web_json:
                detections.dump_web()
            else:
                detections.dump()
        else:
            if not train_mode:
                print('Saving data...')
                with open(args.ap_data_file, 'wb') as f:
                    pickle.dump(ap_data, f)

            return calc_map(ap_data)
    elif args.benchmark:
        print()
        print()
        print('Stats for the last frame:')
        timer.print_stats()
        avg_seconds = frame_times.get_avg()
        print('Average: %5.2f fps, %5.2f ms' % (1 / frame_times.get_avg(), 1000 * avg_seconds))

except KeyboardInterrupt:
    print('Stopping...')

def calc_map(apdata): print('Calculating mAP...') aps = [{'box': [], 'mask': []} for in iou_thresholds]

for _class in range(len(cfg.dataset.class_names)):
    for iou_idx in range(len(iou_thresholds)):
        for iou_type in ('box', 'mask'):
            ap_obj = ap_data[iou_type][iou_idx][_class]

            if not ap_obj.is_empty():
                aps[iou_idx][iou_type].append(ap_obj.get_ap())

all_maps = {'box': OrderedDict(), 'mask': OrderedDict()}

# Looking back at it, this code is really hard to read :/
for iou_type in ('box', 'mask'):
    all_maps[iou_type]['all'] = 0  # Make this first in the ordereddict
    for i, threshold in enumerate(iou_thresholds):
        mAP = sum(aps[i][iou_type]) / len(aps[i][iou_type]) * 100 if len(aps[i][iou_type]) > 0 else 0
        all_maps[iou_type][int(threshold * 100)] = mAP
    all_maps[iou_type]['all'] = (sum(all_maps[iou_type].values()) / (len(all_maps[iou_type].values()) - 1))

print_maps(all_maps)

# Put in a prettier format so we can serialize it to json during training
all_maps = {k: {j: round(u, 2) for j, u in v.items()} for k, v in all_maps.items()}
return all_maps

def print_maps(all_maps):

Warning: hacky

make_row = lambda vals: (' %5s |' * len(vals)) % tuple(vals)
make_sep = lambda n: ('-------+' * n)

print()
print(make_row([''] + [('.%d ' % x if isinstance(x, int) else x + ' ') for x in all_maps['box'].keys()]))
print(make_sep(len(all_maps['box']) + 1))
for iou_type in ('box', 'mask'):
    print(make_row([iou_type] + ['%.2f' % x if x < 100 else '%.1f' % x for x in all_maps[iou_type].values()]))
print(make_sep(len(all_maps['box']) + 1))
print()

if name == 'main': parse_args()

if args.config is not None:
    set_cfg(args.config)

if args.trained_model == 'interrupt':
    args.trained_model = SavePath.get_interrupt('weights/')
elif args.trained_model == 'latest':
    args.trained_model = SavePath.get_latest('weights/', cfg.name)

if args.config is None:
    model_path = SavePath.from_str(args.trained_model)
    # TODO: Bad practice? Probably want to do a name lookup instead.
    args.config = model_path.model_name + '_config'
    print('Config not specified. Parsed %s from the file name.\n' % args.config)
    set_cfg(args.config)

if args.detect:
    cfg.eval_mask_branch = False

if args.dataset is not None:
    set_dataset(args.dataset)

with torch.no_grad():
    if not os.path.exists('results'):
        os.makedirs('results')

    if args.cuda:
        cudnn.fastest = True
        torch.set_default_tensor_type('torch.cuda.FloatTensor')
    else:
        torch.set_default_tensor_type('torch.FloatTensor')

    if args.resume and not args.display:
        with open(args.ap_data_file, 'rb') as f:
            ap_data = pickle.load(f)
        calc_map(ap_data)
        exit()

    if args.image is None and args.video is None and args.images is None:
        dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info,
                                transform=BaseTransform(), has_gt=cfg.dataset.has_gt)
        prep_coco_cats()
    else:
        dataset = None

    print('Loading model...', end='')
    net = Yolact()
    net.load_weights(args.trained_model)
    net.eval()
    print(' Done.')

    if args.cuda:
        net = net.cuda()

    evaluate(net, dataset)

`

Then run `

try on folder of images:

python eval5_images.py --trained_model=weights/yolact_base_547_63000.pth --score_threshold=0.7 --top_k=75 --display_text=False --display_bbox=False --images=train_1920_1100:output_folder --display_masks=True `

and Bob's you uncle

[7eta]

@davodogster thanks share the code. but not working... still run? I tried all copies and paste it at eval.py explode the indentation error...

[davodogster]

Hi @7eta and @areebsyed. Here is the code https://gist.github.com/davodogster/fd102a0bf86d10d877fb76f7be4a1625 I'm not sure if it works with the latest version of Yolact. I mostly use Detectron2 now as it give me better accuracy.

[7eta]

@davodogster wow. very flash! Thanks a lot! but show me IndexError: tuple index out of range, line 148, in prep_display path2 = path_split[1] I was wonder if you can fix it? I'm looking for solution on my way.

I hope to get coco json type file from own image data and then convert coco json to labelme json

[davodogster]

@7eta sorry, I don't think I can help. Add a print statements in the code before that line to check what it's doing. Also update the code with your file paths if needed.

Run it like this and modify the paths and file names: python eval5_images.py --trained_model=weights/yolact_base_547_63000.pth --score_threshold=0.7 --top_k=75 --display_text=False --display_bbox=False --images=train_1920_1100:output_folder --display_masks=True

[7eta]

@davodogster wow... so fast... I changed path_split[1] to path_split[0] and nominate output directory (original none type.. ) now, I get instance mask segmentation image file. but no generate coco json file... : (

[davodogster]

@7eta Great it sounds like it's working properly. You might not have read the next steps above "Then I wrote a script to rename all the images and masks so that it can finally be passed in the exact format to waspinators pycococreator script (hardcode iscrowd = 0) to generate the coco.json from the masks." So you need to install an figure out how to use https://github.com/waspinator/pycococreator . THen you need to just rename your new "instance mask segmentation files" and then pass them to the pycococreator script (and change the file paths to your data)

# clone pycococretor, cd to pycococretor then run: pip install .

import datetime
import json
import os
import re
import fnmatch
from PIL import Image
import numpy as np
from pycococreatortools import pycococreatortools

ROOT_DIR = '/home/davidsos/Documents/datumaro_weds_28/masks_proj-voc'
IMAGE_DIR = os.path.join(ROOT_DIR, "images_out_test")
ANNOTATION_DIR = os.path.join(ROOT_DIR, "annotations_out_test")

from PIL import Image  # (pip install Pillow)

def create_sub_masks_v2(mask_image):
    width, height = mask_image.size

    # Initialize a dictionary of sub-masks indexed by RGB colors
    sub_masks = {}
    for x in range(width):
        for y in range(height):
            # Get the RGB values of the pixel
            pixel = mask_image.getpixel((x, y))[:3]

            # If the pixel is not black...
            if pixel != (0, 0, 0):
                # Check to see if we've created a sub-mask...
                pixel_str = str(pixel)
                sub_mask = sub_masks.get(pixel_str)
                if sub_mask is None:
                    # Create a sub-mask (one bit per pixel) and add to the dictionary
                    # Note: we add 1 pixel of padding in each direction
                    # because the contours module doesn't handle cases
                    # where pixels bleed to the edge of the image
                    sub_masks[pixel_str] = Image.new('1', (width, height)) # + 2, height + 2

                # Set the pixel value to 1 (default is 0), accounting for padding
                sub_masks[pixel_str].putpixel((x, y), 1) # +1 , +1
    return sub_masks

INFO = {
    "description": "Example Dataset",
    "url": "https://github.com/waspinator/pycococreator",
    "version": "0.1.0",
    "year": 2018,
    "contributor": "waspinator",
    "date_created": datetime.datetime.utcnow().isoformat(' ')
}

LICENSES = [
    {
        "id": 1,
        "name": "Attribution-NonCommercial-ShareAlike License",
        "url": "http://creativecommons.org/licenses/by-nc-sa/2.0/"
    }
]

CATEGORIES = [
    {
        'id': 1,
        'name': 'mussel',
        'supercategory': '',
    }
]

def filter_for_jpeg(root, files):
    file_types = ['*.jpeg', '*.jpg']
    file_types = r'|'.join([fnmatch.translate(x) for x in file_types])
    files = [os.path.join(root, f) for f in files]
    files = [f for f in files if re.match(file_types, f)]

    return files

def filter_for_annotations(root, files, image_filename):
    file_types = ['*.png']
    file_types = r'|'.join([fnmatch.translate(x) for x in file_types])
    basename_no_extension = os.path.splitext(os.path.basename(image_filename))[0]
    file_name_prefix = basename_no_extension + '.*'
    files = [os.path.join(root, f) for f in files]
    files = [f for f in files if re.match(file_types, f)]
    files = [f for f in files if re.match(file_name_prefix, os.path.splitext(os.path.basename(f))[0])]

    return files

# def main():

coco_output = {
    "info": INFO,
    "licenses": LICENSES,
    "categories": CATEGORIES,
    "images": [],
    "annotations": []
}

image_id = 1
segmentation_id = 1

# filter for jpeg images
for root, _, files in os.walk(IMAGE_DIR):
    image_files = filter_for_jpeg(root, files)

    # go through each image
    for image_filename in image_files:
        image = Image.open(image_filename)
        image_info = pycococreatortools.create_image_info(
            image_id, os.path.basename(image_filename), image.size)
        coco_output["images"].append(image_info)

        # filter for associated png annotations
        for root, _, files in os.walk(ANNOTATION_DIR):
            annotation_files = filter_for_annotations(root, files, image_filename)

            # go through each associated annotation
            for annotation_filename in annotation_files:

                print(annotation_filename)
                class_id = [x['id'] for x in CATEGORIES if x['name'] in annotation_filename][0]

                category_info = {'id': class_id, 'is_crowd': 0} # hardcoded. 'crowd' in image_filename
                binary_mask = np.asarray(Image.open(annotation_filename)
                                         .convert('1')).astype(np.uint8)

                annotation_info = pycococreatortools.create_annotation_info(
                    segmentation_id, image_id, category_info, binary_mask,
                    image.size, tolerance=2)

                if annotation_info is not None:
                    coco_output["annotations"].append(annotation_info)

                segmentation_id = segmentation_id + 1

        image_id = image_id + 1

with open('{}/cropped_instances_test_is0.json'.format(ROOT_DIR), 'w') as output_json_file:
    json.dump(coco_output, output_json_file)

Rename images and annotations similar to this (but modify it for pycococreator format)

`

rename images

import os

path = '/home/davidsos/Documents/SciVision/mounds4/yolo2' paths = os.listdir(path); print(paths)

os.mkdir(os.path.join(path, 'images')) for path in paths: if ".jpg" in path: outname = os.path.join(path, 'images', path.replace("frame", "frame2")) print(path_) print(out_name)

TODO mkdir 'images

    os.rename(os.path.join(path, path_), out_name)

rename annotations

os.mkdir(os.path.join(path, 'labels')) for path in paths: if ".txt" in path: outname = os.path.join(path, 'labels', path.replace("frame", "frame2")) print(path_) print(out_name)

TODO: mkdir 'labels'

    os.rename(os.path.join(path, path_), out_name)

`

[7eta]

yes sir! tanks a lot

[qiumei1101]

yes sir! tanks a lot

Hi, can you get the correct json file with code?

[MiguelAngeloMartinsRibeiro]

@davodogster Hi Sam, could you get the masks.png from Yolact with your nee eval.py? I mean you pass a folder of images and your output is a binary mask.png?

[davodogster]

Hi @MiguelAngeloMartinsRibeiro Yes https://gist.github.com/davodogster/fd102a0bf86d10d877fb76f7be4a1625 will output a binary mask for every instance/object for every image. Then you can check my other comment about installing and using waspinator and renaming the images and annotations to be in the correct format. I also only used a single class so it may have to be modified a little for multiple classes. Cheers

[didnmj]

@MiguelAngeloMartinsRibeiro

Each object does not have a different name, and all appear as image.jpg_mask0.png. Can each object have a different name?