# Code adapted from Tensorflow Object Detection Framework
# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
# Tensorflow Object Detection Detector

import numpy as np
import tensorflow as tf
import cv2
import os 
import json

from Application.Classifiers.ClassifierInterface import ClassifierInterface


class Classifier(ClassifierInterface):
    def __init__(self):
        self.threshold = .5
        with open(os.path.join(os.path.dirname(__file__), "coco_map.json")) as file:
            mapping = json.load(file)
            self.classes = dict()
            for element in mapping:
                self.classes[element["id"]-1] = element["display_name"]

        self.net = cv2.dnn.readNet(os.path.join(os.path.dirname(__file__),"yolov4.weights"),os.path.join(os.path.dirname(__file__),"yolov4.cfg"))
        #self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
        #self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
        self.layer_names = self.net.getLayerNames()
        self.outputlayers = [self.layer_names[i[0] - 1] for i in self.net.getUnconnectedOutLayers()]   

        print("Classifier Initiated")  

    def tagLayer(self, imgs):
        # get the results from the net

        results = []
        for i, contours in enumerate(imgs[19:20]):
            #print(i)
            for contour in contours:
                height,width,channels = contour.shape
  
                dim = max(height, width)
                if dim > 320:
                    img2 = np.zeros(shape=[dim, dim, 3], dtype=np.uint8)  
                else:
                    img2 = np.zeros(shape=[320,320, 3], dtype=np.uint8)  
                img2[:height,:width] = contour
                blob = cv2.dnn.blobFromImage(img2,1/256,(320,320),(0,0,0),True,crop=False) #reduce 416 to 320    
                self.net.setInput(blob)
                outs = self.net.forward(self.outputlayers)
                for out in outs:
                    for detection in out:
                        scores = detection
                        class_id = np.argmax(scores)
                        confidence = scores[class_id]
                        if confidence > self.threshold:
                            if self.classes[class_id] not in results:
                                cv2.imshow("changes x", img2)
                                cv2.waitKey(10) & 0XFF
                                results.append(self.classes[class_id])
                            #print(self.classes[x], score)
            
        return results