#
# Paravision based face matcher
#
import json
import sys

### export PYTHONPATH=/wherever/yoloserv/modules ... as long as "paravision/.../" is in there
from paravision.recognition.exceptions import ParavisionException
from paravision.recognition.engine import Engine
from paravision.recognition.sdk import SDK
import paravision.recognition.utils as pru
#from openvino.inference_engine import Engineq

#from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
from faceclass import FaceClass


 
class Paravision(FaceClass):

    def init(self,backend=None,model=None):
        print("@@@ initialising paravision")
        try:
            self.sdk = SDK(engine=Engine.AUTO)
        except ParavisionException:
            pass


    # @doc find all the faces in the named image
    def detect(self, name):
        boxes = []
        self.boxes = []
        print("** face_recognition::detect ... %s" % name)     
        try:
            # Get all faces from images with qualities, landmarks, and embeddings
            res = self.sdk.get_faces([self.imgs[name]])
            boxes = res.faces
            for b in boxes:
                b = b.bounding_box
                #print(b)
                self.boxes.append((int(b.origin.x),int(b.origin.y),int(b.origin.x+b.width),int(b.origin.y+b.height)))
            print("found %d boxes for %s" % (len(self.boxes), name) )
        except Exception as ex:
            self.errstr = "image processing exception at get_faces: "+str(ex)
            return '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }' % str(ex)
        return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s }' % (len(self.boxes), json.dumps(self.boxes))



    # @doc compare two named images, previously loaded
    def compare(self, name1, name2):   
        print("** face_recognition::compare ... %s vs %s" % (name1,name2))
        self.encs[name1] = self.sdk.get_embedding_from_prepared_image(self.imgs[name1])
        self.encs[name2] = self.sdk.get_embedding_from_prepared_image(self.imgs[name2])
        #if self.encs[name1]==[]:
        #    return '{ "status":14330, "remark":"could not encode image", "guilty_param":"img1", "guilty_value":"%s" }' % name1 
        #if self.encs[name2]==[]:
        #    return '{ "status":14331, "remark":"could not encode image", "guilty_param":"img2", "guilty_value":"%s" }' % name2 
        print(self.encs.keys())
        print(self.encs[name1])
        print(self.encs[name2])
        try:
            res = self.sdk.get_match_score(self.model, other.model)
            print("Match is ",res)
            #self.match_score = 1000 * (1 - face_recognition.face_distance([self.encs[name1][0]], self.encs[name2][0]))            
            #print("Score is ",self.match_score)
            
            # Create .json
            self.tree["score"] = self.match_score[0]
        except Exception as ex:
            print("** paravision::compare exception ... " + str(ex) )
            self.errstr =  "image comparison exception at compute_scores: "+str(ex)
            return '{ "status":332410, "remark":"%s" }' % self.errstr
        return '{ "status":0, "remark":"OK", "score":%d }' % self.match_score[0] 

   


if __name__ == '__main__':
    
    d = Paravision()
    d.init()

    if sys.argv[1]=="messia":
        jsonstr = d.load1("pic1", "testimg/messi4.jpg")
        print(jsonstr)
        jsonstr = d.detect("pic1")
        print(jsonstr)

    if sys.argv[1]=="test":
        d.load1("pic1", "testimg/ox.jpg")
        d.detect("pic1")

    if sys.argv[1]=="kiosk":
        jsonstr = d.crowd_vs_govid("pic1", "testimg/ox.jpg", 0, "pic2", "testimg/ox_govid.jpg", 0.25)
        print(jsonstr)

    if sys.argv[1]=="messi":
        jsonstr = d.crowd_vs_govid("pic1", "testimg/messi4.jpg", 0, "pic2", "testimg/messi2.jpg", 0)
        print(jsonstr)

    if sys.argv[1]=="maiden":
        jsonstr = d.crowd_vs_govid("pic1", "testimg/ironmaiden.jpg", 0, "pic2", "testimg/davemurray.jpg", 0)
        print(jsonstr)