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| Original file line number | Diff line number | Diff line change |
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| @@ -1,4 +1,5 @@ | ||
| from __future__ import absolute_import | ||
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| from . import coco | ||
| from . import imagenet | ||
| from . import voc |
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Learn more about bidirectional Unicode characters
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| person | ||
| bicycle | ||
| car | ||
| motorbike | ||
| aeroplane | ||
| bus | ||
| train | ||
| truck | ||
| boat | ||
| traffic light | ||
| fire hydrant | ||
| stop sign | ||
| parking meter | ||
| bench | ||
| bird | ||
| cat | ||
| dog | ||
| horse | ||
| sheep | ||
| cow | ||
| elephant | ||
| bear | ||
| zebra | ||
| giraffe | ||
| backpack | ||
| umbrella | ||
| handbag | ||
| tie | ||
| suitcase | ||
| frisbee | ||
| skis | ||
| snowboard | ||
| sports ball | ||
| kite | ||
| baseball bat | ||
| baseball glove | ||
| skateboard | ||
| surfboard | ||
| tennis racket | ||
| bottle | ||
| wine glass | ||
| cup | ||
| fork | ||
| knife | ||
| spoon | ||
| bowl | ||
| banana | ||
| apple | ||
| sandwich | ||
| orange | ||
| broccoli | ||
| carrot | ||
| hot dog | ||
| pizza | ||
| donut | ||
| cake | ||
| chair | ||
| sofa | ||
| pottedplant | ||
| bed | ||
| diningtable | ||
| toilet | ||
| tvmonitor | ||
| laptop | ||
| mouse | ||
| remote | ||
| keyboard | ||
| cell phone | ||
| microwave | ||
| oven | ||
| toaster | ||
| sink | ||
| refrigerator | ||
| book | ||
| clock | ||
| vase | ||
| scissors | ||
| teddy bear | ||
| hair drier | ||
| toothbrush |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,204 @@ | ||
| """Collection of MS COCO utils | ||
| The codes were adapted from [py-faster-rcnn](https://github.com/ | ||
| rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py). | ||
| """ | ||
| from __future__ import division | ||
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| import os | ||
| import json | ||
| import numpy as np | ||
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| try: | ||
| import cv2 | ||
| except ImportError: | ||
| cv2 = None | ||
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| try: | ||
| from pycocotools.coco import COCO | ||
| except ImportError: | ||
| COCO = None | ||
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| try: | ||
| xrange # Python 2 | ||
| except NameError: | ||
| xrange = range # Python 3 | ||
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| metas = {} | ||
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| with open(os.path.join(os.path.dirname(__file__), 'coco.names'), 'r') as f: | ||
| classnames = [line.rstrip() for line in f.readlines()] | ||
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| def classidx(classname): | ||
| return dict((k, i) for (i, k) in enumerate(classnames))[classname] | ||
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| def area(box): | ||
| if box.ndim == 1: | ||
| return (box[2] - box[0] + 1.) * (box[3] - box[1] + 1.) | ||
| else: | ||
| return (box[:, 2] - box[:, 0] + 1.) * (box[:, 3] - box[:, 1] + 1.) | ||
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| def get_files(data_dir, data_name, total_num=None): | ||
| assert COCO is not None, '`datasets.coco` requires `pycocotools`.' | ||
| if data_name not in metas: | ||
| metas[data_name] = COCO("%s/annotations/instances_%s.json" % | ||
| (data_dir, data_name)) | ||
| images = metas[data_name].imgs | ||
| fileids = images.keys() | ||
| if total_num is not None: | ||
| fileids = fileids[:total_num] | ||
| files = [images[i]['file_name'] for i in fileids] | ||
| return fileids, files | ||
|
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| def get_annotations(data_dir, data_name, ids): | ||
| assert COCO is not None, '`datasets.coco` requires `pycocotools`.' | ||
| if data_name not in metas: | ||
| metas[data_name] = COCO("%s/annotations/instances_%s.json" % | ||
| (data_dir, data_name)) | ||
| cmap = dict([(b, a) for (a, b) in enumerate(metas[data_name].getCatIds())]) | ||
| annotations = {} | ||
| for i in ids: | ||
| annids = metas[data_name].getAnnIds(imgIds=i, iscrowd=None) | ||
| objs = metas[data_name].loadAnns(annids) | ||
| annotations[i] = [[] for _ in range(80)] | ||
| width = metas[data_name].imgs[i]['width'] | ||
| height = metas[data_name].imgs[i]['height'] | ||
| valid_objs = [] | ||
| for obj in objs: | ||
| x1 = np.max((0, obj['bbox'][0])) | ||
| y1 = np.max((0, obj['bbox'][1])) | ||
| x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1)))) | ||
| y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1)))) | ||
| if obj['area'] > 0 and x2 >= x1 and y2 >= y1: | ||
| obj_struct = {'bbox': [x1, y1, x2, y2]} | ||
| cidx = cmap[obj['category_id']] | ||
| annotations[i][cidx].append(obj_struct) | ||
| return annotations | ||
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| def load(data_dir, data_name, min_shorter_side=None, max_longer_side=1000, | ||
| batch_size=1, total_num=None): | ||
| assert cv2 is not None, '`load` requires `cv2`.' | ||
| _, files = get_files(data_dir, data_name, total_num) | ||
| total_num = len(files) | ||
|
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| for batch_start in range(0, total_num, batch_size): | ||
| x = cv2.imread("%s/%s/%s" % (data_dir, data_name, files[batch_start])) | ||
| if min_shorter_side is not None: | ||
| scale = float(min_shorter_side) / np.min(x.shape[:2]) | ||
| else: | ||
| scale = 1.0 | ||
| if round(scale * np.max(x.shape[:2])) > max_longer_side: | ||
| scale = float(max_longer_side) / np.max(x.shape[:2]) | ||
| x = cv2.resize(x, None, None, fx=scale, fy=scale, | ||
| interpolation=cv2.INTER_LINEAR) | ||
| x = np.array([x], dtype=np.float32) | ||
| scale = np.array([scale], dtype=np.float32) | ||
| yield x, scale | ||
| del x | ||
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| def evaluate_class(ids, scores, boxes, annotations, files, ovthresh): | ||
| if scores.shape[0] == 0: | ||
| return 0.0, np.zeros(len(ids)), np.zeros(len(ids)) | ||
|
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| # extract gt objects for this class | ||
| diff = [np.array([0 for obj in annotations[filename]]) | ||
| for filename in files] | ||
| total = sum([sum(x == 0) for x in diff]) | ||
| detected = dict(zip(files, [[False] * len(x) for x in diff])) | ||
|
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| # sort by confidence | ||
| sorted_ind = np.argsort(-scores) | ||
| ids = ids[sorted_ind] | ||
| boxes = boxes[sorted_ind, :] | ||
|
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| # go down dets and mark TPs and FPs | ||
| tp_list = [] | ||
| fp_list = [] | ||
| for d in range(len(ids)): | ||
| actual = np.array([x['bbox'] for x in annotations[ids[d]]]) | ||
| difficult = np.array([0 for x in annotations[ids[d]]]) | ||
|
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| if actual.size > 0: | ||
| iw = np.maximum(np.minimum(actual[:, 2], boxes[d, 2]) - | ||
| np.maximum(actual[:, 0], boxes[d, 0]) + 1, 0) | ||
| ih = np.maximum(np.minimum(actual[:, 3], boxes[d, 3]) - | ||
| np.maximum(actual[:, 1], boxes[d, 1]) + 1, 0) | ||
| inters = iw * ih | ||
| overlaps = inters / (area(actual) + area(boxes[d, :]) - inters) | ||
| jmax = np.argmax(overlaps) | ||
| ovmax = overlaps[jmax] | ||
| else: | ||
| ovmax = -np.inf | ||
|
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| tp = 0. | ||
| fp = 0. | ||
| if ovmax > ovthresh: | ||
| if difficult[jmax] == 0: | ||
| if not detected[ids[d]][jmax]: | ||
| tp = 1. | ||
| detected[ids[d]][jmax] = True | ||
| else: | ||
| fp = 1. | ||
| else: | ||
| fp = 1. | ||
| tp_list.append(tp) | ||
| fp_list.append(fp) | ||
|
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| tp = np.cumsum(tp_list) | ||
| fp = np.cumsum(fp_list) | ||
| recall = tp / float(total) | ||
| precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) | ||
| ap = np.mean([0 if np.sum(recall >= t) == 0 | ||
| else np.max(precision[recall >= t]) | ||
| for t in np.linspace(0, 1, 11)]) | ||
|
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| return ap, precision, recall | ||
|
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|
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| def evaluate(results, data_dir, data_name, ovthresh=0.5, verbose=True): | ||
| fileids, _ = get_files(data_dir, data_name) | ||
| fileids = fileids[:len(results)] | ||
| annotations = get_annotations(data_dir, data_name, fileids) | ||
| aps = [] | ||
|
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| for c in range(80): | ||
| ids = [] | ||
| scores = [] | ||
| boxes = [] | ||
| for (i, fileid) in enumerate(fileids): | ||
| pred = results[i][c] | ||
| if pred.shape[0] > 0: | ||
| for k in xrange(pred.shape[0]): | ||
| ids.append(fileid) | ||
| scores.append(pred[k, -1]) | ||
| boxes.append(pred[k, :4] + 1) | ||
| ids = np.array(ids) | ||
| scores = np.array(scores) | ||
| boxes = np.array(boxes) | ||
| _annotations = dict((k, v[c]) for (k, v) in annotations.iteritems()) | ||
| ap, _, _ = evaluate_class(ids, scores, boxes, _annotations, | ||
| fileids, ovthresh) | ||
| aps += [ap] | ||
|
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||
| strs = '' | ||
| for c in range(80): | ||
| strs += "| %6s " % classnames[c][:6] | ||
| strs += '|\n' | ||
|
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| for ap in aps: | ||
| strs += '|--------' | ||
| strs += '|\n' | ||
|
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| for ap in aps: | ||
| strs += "| %.4f " % ap | ||
| strs += '|\n' | ||
|
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| strs += "Mean = %.4f" % np.mean(aps) | ||
| return strs |