How to use the pyclowder.extractors.download_file_metadata_jsonld function in pyclowder

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github terraref / computing-pipeline / scripts / plantcv / extractor_with_avg_traits / terra.plantcv.py View on Github external
nir_traits = {}
    vis_traits = {}

    # build img paths list
    img_paths = []
    # get imgs paths, filter out the json paths
    for p in parameters['files']:
        if p[-4:] == '.jpg' or p[-4:] == '.png':
            img_paths.append(p)
    print "printing img_paths..."
    print img_paths

    # build file objs - list of dicts
    file_objs = []
    for f in parameters['filelist']:
        fmd = (extractors.download_file_metadata_jsonld(parameters['host'], parameters['secretKey'], f['id'], extractorName))[0]
        #print "printing fmd..."
        #print fmd
        angle = fmd['content']['rotation_angle']        # -1, 0, 90, 180, 270
        perspective = fmd['content']['perspective']        # 'side-view' / 'top-view'
        if perspective == 'top-view': angle = -1        # set tv angle to be -1 for later sorting        
        camera_type = fmd['content']['camera_type']        # 'visible/RGB' / 'near-infrared'
        image_id = f['id']
        for p in img_paths:
            print "printing p.."
            print p 
            path =  (re.findall(str(image_id), p))
            print "printing path founded.."
            print path
            if path != []:
                file_objs.append({'perspective':perspective, 'angle':angle, 'camera_type':camera_type, 'image_path': p, 'image_id': image_id})
github terraref / computing-pipeline / scripts / plantcv / extractor / terra.plantcv.py View on Github external
(fields, traits) = pcia.get_traits_table()

    # get imgs paths, filter out the json paths
    img_paths = []
    for p in parameters['files']:
        if p[-4:] == '.jpg' or p[-4:] == '.png':
            img_paths.append(p)

    # build list of file descriptor dictionaries with sensor info
    file_objs = []
    for f in parameters['filelist']:
        found_info = False
        image_id = f['id']
        # Get from file metadata if possible
        file_md = extractors.download_file_metadata_jsonld(parameters['host'], parameters['secretKey'], f['id'])
        for md in file_md:
            if 'content' in md:
                mdc = md['content']
                if ('rotation_angle' in mdc) and ('perspective' in mdc) and ('camera_type' in mdc):
                    found_info = True
                    # perspective = 'side-view' / 'top-view'
                    perspective = mdc['perspective']
                    # angle = -1, 0, 90, 180, 270; set top-view angle to be -1 for later sorting
                    angle = mdc['rotation_angle'] if perspective != 'top-view' else -1
                    # camera_type = 'visible/RGB' / 'near-infrared'
                    camera_type = mdc['camera_type']

                    for pth in img_paths:
                        if re.findall(str(image_id), pth) != []:
                            file_objs.append({
                                'perspective': perspective,