Metadata-Version: 2.1
Name: syncvtools
Version: 0.1.13
Summary: CV Tools related to Object Detection
Home-page: UNKNOWN
Author: Aleksandr Patsekin
Author-email: apatsekin@gmail.com
License: UNKNOWN
Description: # Synapse Computer Vision Tools
        More details are provided in Notion page. 
        
        ## Requirements
        - Python 3.5+
        - OpenCV
        - Pillow
        - LXML
        - For TF Records - TensorFlow
        
        ## Installation
        Using PIP
        
            pip3 import syncvtools
        
        ### Visualize predictions from prod
        
        ```
        from syncvtools.utils.draw_detections import DrawDetections
        from syncvtools.utils.parsers import ProdDetections, TFRecords, TFObjDetAPIDetections
        
        prod_dets = ProdDetections.parse_prod_detections(img_dir='IMAGE_DIRECTORY', predictions_dir='DETECTIONS_FILE_DIR')
        drawer = DrawDetections(bbox_line_height=1, threshold=0.5)
        for pro_det in prod_dets:
            vis_img = drawer.draw_imageleveldetections(img_dets=prod_dets[pro_det])
            cv2.imshow("predictions", vis_img)
            cv2.waitKey(0)
        ```
        
        
        ### Visualize predictions from TF training
        ```
        from syncvtools.utils.draw_detections import DrawDetections
        from syncvtools.utils.parsers import ProdDetections, TFRecords, TFObjDetAPIDetections
        
        tf_inf = TFObjDetAPIDetections.parse_detections(detection_file='detections_and_losses.json')
        tf_gts = TFRecords.parse(tfrecord_src='val.tfrecord')
        #adding image info to predictions/gt
        tf_inf += tf_gts
        if label_map is not None:
            tf_inf.process_labelmap('path_to_label_map.pbtxt')
        
        drawer = DrawDetections(bbox_line_height=1, threshold=0.5)
        
        
        for pro_det in tf_inf:
            if not tf_inf[pro_det].detections and not tf_inf[pro_det].ground_truth:
                continue #no gt/detections here
            vis_img = drawer.draw_imageleveldetections(img_dets=tf_inf[pro_det])
            cv2.imshow("predictions/gt from TF inference", vis_img)
            cv2.waitKey(0)
        ```
        
        # utils
        
        ```
        from syncvtools.utils import file_tools as ft
        
        ft.get_file_list_by_ext(dir='input_path', ext=('jpg','png'))
        ```
        
        Returns a list of string with full path to files with `ext` extension.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
