Python COCO.showAnns方法代码示例

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
class CocoUtils(object):
    def __init__(self, data_dir, data_type):
        ann_file = '%s/annotations/instances_%s.json' % (data_dir, data_type)
        # initialize COCO api for instance annotations
        self.coco = COCO(ann_file)

    def get_img_annotations(self, pic_id):
        ann_ids = self.coco.getAnnIds(imgIds=pic_id, iscrowd=None)
        return self.coco.loadAnns(ann_ids)

    def get_mask_array_and_image(self, annotation, img_width, img_height, fill_color):
        seg = annotation['segmentation']
        raster_img = Image.new('L', (img_width, img_height), 0)
        for polyg in seg:
            ImageDraw.Draw(raster_img).polygon(polyg, outline=fill_color, fill=fill_color)
        return np.array(raster_img), raster_img

    def get_annotation_mask(self, annotation, img_width, img_height):
        seg_mask, seg_img = self.get_mask_array_and_image(annotation, img_width, img_height, 1)
        return seg_mask

    # mask true's are 1 but image true's are 128- otherwise it's pretty much invisible
    def get_annotation_image(self, annotation, img_width, img_height):
        seg_mask, seg_img = self.get_mask_array_and_image(annotation, img_width, img_height, mask_pic_true_color)
        return seg_img

    def are_legal_anotations(self, annotations):
        # unfortunately, only polygon segmentations work for now (RLE mask type decoding causes a python crash)
        polygon_segmentations = ['segmentation' in ann and type(ann['segmentation']) == list for ann in annotations]
        return all(polygon_segmentations)

    def show_annotations(self, pic_path, annotations):
        if self.are_legal_anotations(annotations):
            pylab.rcParams['figure.figsize'] = (10.0, 8.0)
            read_img = io.imread(pic_path)
            plt.figure()
            plt.imshow(read_img)
            self.coco.showAnns(annotations)
        else:
            print 'cannot show invalid annotation'

    def get_images_data(self):
        # each item is image_id, image_file_name
        return [pic_data[1] for pic_data in self.coco.imgs.items()]

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
for imgId in imgIds:

    imgIds = coco.getImgIds(imgIds = [2018019951])      # 555705, cat
    img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]

    # load and display image
    # I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))
    # use url to load image
    I = io.imread(imgDir + img['file_name'])
    # load and display instance annotations
    plt.imshow(I); 
    plt.axis('off')
    annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
    anns = coco.loadAnns(annIds)
    print(anns)
    # display mask
    coco.showAnns(anns)
    plt.show()

# display bbox
# img = cv2.imread(imgDir + img['file_name'])
# bbox = anns[1]['bbox']
# print(bbox)
# cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])), (255, 0, 0))
# # cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 3)

# cv2.imshow('demo', img)
# cv2.waitKey(8000)

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
nms = set([cat['supercategory'] for cat in cats]) #test=[cat['supercategory'] for cat in cats]
print 'COCO supercategoires :\n', ' '.join(nms)

# get all images of some given category
catIds = train.getCatIds(catNms=['person', 'dog', 'skateboard'])
imgIds = train.getImgIds(catIds=catIds)

# load and display image
for i in range(0, 5):
	img = train.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
	I = io.imread('%s/images/%s/%s'%(dataDir, trainDataType, img['file_name']))
	print '%s/images/%s/%s'%(dataDir, trainDataType, img['file_name'])
	plt.figure()
	plt.imshow(I)
	plt.show()

plt.imshow(I)
annIds = train.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = train.loadAnns(annIds)
train.showAnns(anns)

# for caption annotations
annFile = '%s/annotations/captions_%s.json'%(dataDir, trainDataType)
caps = COCO(annFile)

annIds = caps.getAnnIds(imgIds=img['id'])
anns = caps.loadAnns(annIds)
caps.showAnns(anns)
plt.imshow(I)
plt.show()

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
subject_id = interaction["subject_id"]
subject_anns = coco.loadAnns(subject_id)[0]

object_id = interaction["object_id"]
object_anns = coco.loadAnns(object_id)[0]
object_cat = coco.cats[object_anns["category_id"]]["name"]

v_actions = interaction["visual_actions"]
v_adverbs = interaction["visual_adverbs"]

print("Image ID:  [{0}]".format(image_id))
print("Subject ID:[{0}]".format(subject_id))
print("Object ID: [{0}], Category: [{1}]".format(object_id, object_cat))

print("\nVisual Actions:")
for va_id in v_actions:
    va = [x for x in visual_actions if x["id"] == va_id][0]
    print("  - id:[{0}], name:[{1}]".format(va["id"], va["name"]))

print("\nVisual Adverbs:")
for va_id in v_adverbs:
    va = [x for x in visual_adverbs if x["id"] == va_id][0]
    print("  - id:[{0}], name:[{1}]".format(va["id"], va["name"]))

img = coco.loadImgs(image_id)[0]
I = io.imread("{0}/{1}/{2}".format(COCO_IMG_DIR, "train2014", img["file_name"]))
plt.figure(figsize=(12, 8))
plt.imshow(I)
coco.showAnns([subject_anns, object_anns])
plt.show()

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]

#.........这里部分代码省略.........
        data = []

        # loop through each image
        for imgId in imgIds:
            item = DataItem()

            img = self.coco.loadImgs(imgId)[0]
            item.im_path = "%s/images/%s/%s"%(dataset, dataset_phase, img["file_name"])
            item.im_size = [3, img["height"], img["width"]]
            item.coco_id = imgId
            annIds = self.coco.getAnnIds(imgIds=img['id'], iscrowd=False)
            anns = self.coco.loadAnns(annIds)

            all_person_keypoints = []
            masked_persons_RLE = []
            visible_persons_RLE = []
            all_visibilities = []

            # Consider only images with people
            has_people = len(anns) > 0
            if not has_people and self.cfg.coco_only_images_with_people:
                continue

            for ann in anns: # loop through each person
                person_keypoints = []
                visibilities = []
                if ann["num_keypoints"] != 0:
                    for i in range(self.cfg.num_joints):
                        x_coord = ann["keypoints"][3 * i]
                        y_coord = ann["keypoints"][3 * i + 1]
                        visibility = ann["keypoints"][3 * i + 2]
                        visibilities.append(visibility)
                        if visibility != 0: # i.e. if labeled
                            person_keypoints.append([i, x_coord, y_coord])
                    all_person_keypoints.append(np.array(person_keypoints))
                    visible_persons_RLE.append(maskUtils.decode(self.coco.annToRLE(ann)))
                    all_visibilities.append(visibilities)
                if ann["num_keypoints"] == 0:
                    masked_persons_RLE.append(self.coco.annToRLE(ann))

            item.joints = all_person_keypoints
            item.im_neg_mask = maskUtils.merge(masked_persons_RLE)
            if self.cfg.use_gt_segm:
                item.gt_segm = np.moveaxis(np.array(visible_persons_RLE), 0, -1)
                item.visibilities = all_visibilities
            data.append(item)

        self.has_gt = self.cfg.dataset is not "image_info"
        return data


    def compute_scmap_weights(self, scmap_shape, joint_id, data_item):
        size = scmap_shape[0:2]
        scmask = np.ones(size)
        m = maskUtils.decode(data_item.im_neg_mask)
        if m.size:
            scmask = 1.0 - imresize(m, size)
        scmask = np.stack([scmask] * self.cfg.num_joints, axis=-1)
        return scmask

    def get_pose_segments(self):
       return [[0, 1], [0, 2], [1, 3], [2, 4], [5, 7], [6, 8], [7, 9], [8, 10], [11, 13], [12, 14], [13, 15], [14, 16]]

    def visualize_coco(self, coco_img_results, visibilities):
        inFile = "tmp.json"
        with open(inFile, 'w') as outfile:
            json.dump(coco_img_results, outfile)
        get_gt_visibilities(inFile, visibilities)

        # initialize cocoPred api
        cocoPred = self.coco.loadRes(inFile)
        os.remove(inFile)

        imgIds = [coco_img_results[0]["image_id"]]

        for imgId in imgIds:
            img = cocoPred.loadImgs(imgId)[0]
            im_path = "%s/images/%s/%s" % (self.cfg.dataset, self.cfg.dataset_phase, img["file_name"])
            I = io.imread(im_path)

            fig = plt.figure()
            a = fig.add_subplot(2, 2, 1)
            plt.imshow(I)
            a.set_title('Initial Image')

            a = fig.add_subplot(2, 2, 2)
            plt.imshow(I)
            a.set_title('Predicted Keypoints')
            annIds = cocoPred.getAnnIds(imgIds=img['id'])
            anns = cocoPred.loadAnns(annIds)
            cocoPred.showAnns(anns)

            a = fig.add_subplot(2, 2, 3)
            plt.imshow(I)
            a.set_title('GT Keypoints')
            annIds = self.coco.getAnnIds(imgIds=img['id'])
            anns = self.coco.loadAnns(annIds)
            self.coco.showAnns(anns)

            plt.show()

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
# In[6]:

# load and display image
I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))
plt.figure()
plt.imshow(I)


# In[7]:

# load and display instance annotations
plt.imshow(I)
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)


# In[8]:

# initialize COCO api for caption annotations
annFile = '%s/annotations/captions_%s.json'%(dataDir,dataType)
caps=COCO(annFile)


# In[9]:

# load and display caption annotations
annIds = caps.getAnnIds(imgIds=img['id']);
anns = caps.loadAnns(annIds)
caps.showAnns(anns)

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
# load and display instance annotations
plt.imshow(I)
plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)    # iscrowd: anns for given crowd label
print annIds
anns = coco.loadAnns(annIds)    # list, len = 6
# [u'segmentation',
#  u'area',
#  u'iscrowd',
#  u'image_id',
#  u'bbox',
#  u'category_id',
#  u'id']
print anns
coco.showAnns(anns)
plt.show()

# 2) show person keypoints
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir, dataType)
coco_kps = COCO(annFile)

# load and display keypoints
plt.imshow(I)
plt.axis('off')
ax = plt.gca()
annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco_kps.loadAnns(annIds)    # list, len = 6, dict
# [u'segmentation',
#  u'num_keypoints',    # new
#  u'area',

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
class CaptionSaliency:
    def __init__(self,dataType,usingSet,dataDir,savefileDir):
        #setpath
        self.dataType = dataType
        self.usingSet = usingSet
        self.dataDir = dataDir
        self.savefileDir = savefileDir
        self.InsFile='%s/annotations/instances_%s.json'%(dataDir,dataType)
        self.CapFile='%s/annotations/captions_%s.json'%(dataDir,dataType)
        

        self.SALICON = pickle.load(open('%s/%s.p'%(savefileDir,usingSet),'rb'))
        self.Ins_ID = pickle.load(open('%s/Ins_ID_%s.p'%(savefileDir,usingSet),'rb'))
        
        self.category = pickle.load(open('%s/category.p'%savefileDir,'rb'))

        self.category_idx = pickle.load(open('%s/cat_dict_idx.p'%savefileDir,'rb'))#eg., person -- 1
        self.category_supercategory_idx = pickle.load(open('%s/cat_dict_supercat.p'%savefileDir,'rb')) #eg., person--human 
        self.supercategory_idx = pickle.load(open('%s/supercate_id.p'%savefileDir,'rb'))#eg., food--1
        
        self.imsal_dict = pickle.load(open('%s/imsal_dict_%s.p'%(savefileDir,usingSet),'rb'))
        
        self.Ins_coco = COCO(self.InsFile)
        self.Cap_coco = COCO(self.CapFile)
        self.cat_list = self.Ins_coco.cats#category list  (official)
        
        wordmat = sio.loadmat('%s/word_mat_%s.mat'%(savefileDir,usingSet))
        wordmat = wordmat['word_mat']
        self.wordmat = wordmat[:,0]
        
        
        self.correction_list = ['men','man','kid','boy','baby']
        
        self.nounlist = []
        self.nounID = []
        self.Cardi_Noun = []
        self.Seque_Noun = []
        
        
        self.size_norm = float(640*480)
        self.loc_norm = float(math.sqrt(640**2+480**2)) 
        
        
        
        self.saliencydict_c = {}
        self.saliencydict_s = {}
        
        #******************10-03-2016 update***********************
        self.saliencydict_i = {}
        self.transformer = TfidfTransformer()
        
        #******************^^^^^^^10-03-2016 update^^^^^^^^^^***********************
    def show_im(self,image_id):
        if image_id == None:
            raise NameError('no image ID')
        I = io.imread('%s/images/%s/%s'%(self.dataDir,self.dataType,self.SALICON['SALICON_filename'][image_id]))
        plt.imshow(I)
    
    def show_ann(self,image_id):
        if image_id == None:
            raise NameError('no image ID')
        blankim = np.zeros((480,640,3),np.uint8)
        plt.imshow(blankim)
        annIds = self.Ins_coco.getAnnIds(self.SALICON['SALICON_id'][image_id])
        anns = self.Ins_coco.loadAnns(annIds)
        self.Ins_coco.showAnns(anns)
        
    def show_cap(self,image_id):
        if image_id == None:
            raise NameError('no image ID')
        annIds = self.Cap_coco.getAnnIds(self.SALICON['SALICON_id'][image_id])
        anns = self.Cap_coco.loadAnns(annIds)
        self.Cap_coco.showAnns(anns)
    
    
    
    def findID(self,word,im_idd):
        if word in self.category:
            return self.category_idx[word]
        else:
            temp_idlist={}
            for item in self.category_idx.keys():
                for item1 in wn.synsets(item, wn.NOUN):
                    for word1 in wn.synsets(word, wn.NOUN):
                        dist = item1.wup_similarity(word1)
                        if item not in temp_idlist.keys():
                            temp_idlist[self.category_idx[item]] = dist
                            continue
                        if dist > temp_idlist[self.category_idx[item]]:
                            temp_idlist[self.category_idx[item]] = dist
            temp_idlist = sorted(temp_idlist.iteritems(), key=lambda d: d[1], reverse = True)
            temp_idlist = temp_idlist[0:1]
            for n in temp_idlist:
                if n[0] in self.Ins_ID[im_idd]:
                    return n[0]
            return 0
         
         
         
         
#.........这里部分代码省略.........

# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import showAnns [as 别名]
    I = io.imread('%s/val2014/%s'%(dataDir,img['file_name']))
    if len(I.shape) < 3:
        I = np.tile(I[:, :, np.newaxis], (1, 1, 3))

    mask = np.zeros(I.shape, dtype=np.uint8)
    for seg in ann['segmentation']:
        poly = np.array(seg).reshape((len(seg)/2, 2))
        rr, cc = polygon(poly[:, 1], poly[:, 0])
        mask[rr, cc] = [64, 0, 0]

    io.imsave(writeDir + '/images/' + img['file_name'], I)
    io.imsave(writeDir + '/segmentations/' + img['file_name'].replace('jpg', 'png'), mask)
    print 'Wrote file: ', writeDir + '/' + img['file_name']

filenames.close()

imgId = imgIds[np.random.randint(100)]
img = cocoGt.loadImgs(imgId)[0]
I = io.imread('%s/train2014/%s'%(dataDir,img['file_name']))

# visialuze gt and dt side by side
fig = plt.figure(figsize=[15,10])

# ground truth
plt.subplot(121)
plt.imshow(I); plt.axis('off'); plt.title('ground truth')
annIds = cocoGt.getAnnIds(imgIds=imgId, catIds=[17])
anns = cocoGt.loadAnns(annIds)
cocoGt.showAnns(anns)