import os
import glob
import picamera
import cv2
import numpy as np
import importlib.util
from datetime import datetime
import videorecorder as vr
import time
from collections import Counter

# If using TPU, need to load a different library
# from tensorflow.lite.python.interpreter import Interpreter


def take_picture(path):
    if path is None:
        path = "/home/pi/Pictures"
    camera = picamera.PiCamera()
    try:
        camera.capture(os.path.join(path, "image_{0}.jpg".format(datetime.now().strftime('%m%d%Y%H%M%S'))))
    finally:
        print('Picture taken')
        camera.close()

        
def record_video(path=None, cone_color='green', duration=5, runid=0):
    if path is None:
        path="/home/pi/Videos"
    path = os.path.join(path,cone_color)
    try:
        recorder = vr.VideoRecorder(path,runid)
        print('Loaded Video Recorder')
        recorder.start_recording()
        time.sleep(duration)
        recorder.stop_recording()
    except:
        print('Video Recording failed')
    finally:
        print('Video recorded')


class ObjectClassificationModel:

    def __init__(self, model_dir, image_dir, graph_name='detect.tflite', min_conf_threshold=0.5, use_TPU=False):

        self.model_dir = model_dir
        self.image_dir = image_dir
        self.min_conf_threshold = float(min_conf_threshold)
        self.use_TPU = use_TPU
        self._load_model(model_dir=model_dir, graph_name=graph_name)

    def _load_model(self, model_dir, graph_name):
        CWD_PATH = os.getcwd()

        # Load model labels
        PATH_TO_LABELS = os.path.join(CWD_PATH, model_dir, 'labelmap.txt')
        with open(PATH_TO_LABELS, 'r') as f:
            labels = [line.strip() for line in f.readlines()]
        if labels[0] == '???':
            del(labels[0])
        self.labels = labels

        pkg = importlib.util.find_spec('tensorflow')
        if pkg is None:
            from tflite_runtime.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate
        else:
            from tensorflow.lite.python.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate

        # If using Edge TPU, assign filename for Edge TPU model
        if self.use_TPU:
            # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
            if (graph_name == 'detect.tflite'):
                graph_name = 'edgetpu.tflite'

        PATH_TO_CKPT = os.path.join(CWD_PATH, model_dir, graph_name)
        
        # Load the Tensorflow Lite model.
        # If using Edge TPU, use special load_delegate argument
        if self.use_TPU:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT,
                                    experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
            print(PATH_TO_CKPT)
        else:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        self.interpreter.allocate_tensors()

        # Get model details
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        self.height = self.input_details[0]['shape'][1]
        self.width = self.input_details[0]['shape'][2]
        self.floating_model = (self.input_details[0]['dtype'] == np.float32)
        self.input_mean = 127.5
        self.input_std = 127.5

    def classify(self, image_dir):
        images = glob.glob(image_dir + '/*')
        classes_list = []
        scores_list = []
        for image_path in images:
            print('Classifying: {}'.format(image_path))
            
            # Load image and resize to expected shape [1xHxWx3]
            image = cv2.imread(image_path)
            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            imH, imW, _ = image.shape 
            image_resized = cv2.resize(image_rgb, (self.width, self.height))
            input_data = np.expand_dims(image_resized, axis=0)

            # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
            if self.floating_model:
                input_data = (np.float32(input_data) - self.input_mean) / self.input_std

            # Perform the actual detection by running the model with the image as input
            self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
            self.interpreter.invoke()

            # Retrieve detection results
            # We are not using the boxes right now since we do not need to know 
            # where picture the object is, only that it is there.
            # boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
            
            classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
            scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])

        objects_detected = {}
        for classes in classes_list:
            objects = set([self.labels[int(c)] for c in classes])
            for obj in objects:
                if obj in objects_detected.keys():
                    objects_detected[obj] += 1
                else:
                    objects_detected[obj] = 1

        return classes_list, scores_list, objects_detected
    
    
    def classify_video(self, video_dir):
        """Function to detect objects in video file"""
        #1. Get the list of all video files from the directory passed in
        videos = glob.glob(video_dir + '/*')

        #2. Check the number of *.avi files in the folder
        num_videos = len(videos)
        #3. Do not run classification if number of videos in the folder are more than 10 and alert
        if num_videos > 10:
            print('Found more than 10 videos in the directory: {}'.format(video_dir))
            return
        #4. For each video file
        for video_file in videos:
            video_name=os.path.basename(video_file)
            print('Processing video: {}'.format(video_name))
            #4.1 Open the video file 
            video = cv2.VideoCapture(video_file)
            imW = video.get(cv2.CAP_PROP_FRAME_WIDTH)
            imH = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
            collect_labels = []
            #4 .1.1 pass frame by frame to the  detection model
            index = 0
            print('Min Threshold',self.min_conf_threshold)
            while(video.isOpened()):
                # Acquire frame and resize to expected shape [1xHxWx3]
                ret, frame = video.read()
                if frame is None:
                    break
                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame_resized = cv2.resize(frame_rgb, (self.width, self.height))
                input_data = np.expand_dims(frame_resized, axis=0)
                
                # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
                if self.floating_model:
                    input_data = (np.float32(input_data) - self.input_mean) / self.input_std

                # Perform the actual detection by running the model with the image as input
                self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
                self.interpreter.invoke()

                # Retrieve detection results
                boxes = self.interpreter.get_tensor(self.output_details[0]['index'])[0] # Bounding box coordinates of detected objects
                classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
                scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects

                # Loop over all detections and draw detection box if confidence is above minimum threshold
                for i in range(len(scores)):
                    if ((scores[i] > self.min_conf_threshold) and (scores[i] <= 1.0)):

                        # Get bounding box coordinates and draw box
                        # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
                        ymin = int(max(1,(boxes[i][0] * imH)))
                        xmin = int(max(1,(boxes[i][1] * imW)))
                        ymax = int(min(imH,(boxes[i][2] * imH)))
                        xmax = int(min(imW,(boxes[i][3] * imW)))
                        
                        cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 4)

                        # Draw label
                        object_name = self.labels[int(classes[i])] # Look up object name from "labels" array using class index
                        label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
                        labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
                        label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
                        cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
                        cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
                        collect_labels.append(object_name)
                index += 1
                # All the results have been drawn on the frame, so it's time to display it.
                cv2.imshow('Object detector', frame)
            video.release()
            #cv2.destroyAllWindows()
            if len(collect_labels)>0:
                most_common,num_most_common = Counter(collect_labels).most_common(1)[0]
                max_object = most_common
            else:
                max_object = 'Nothing'
            print('Maximum detected object :{}'.format(max_object))
            print(Counter(collect_labels))
        return max_object


class ConeClassificationModel:
    def __init__(self, model_dir, image_dir, graph_name='cone_detect.tflite', min_conf_threshold=0.5, use_TPU=False):

        self.model_dir = model_dir
        self.image_dir = image_dir
        self.min_conf_threshold = float(min_conf_threshold)
        self.use_TPU = use_TPU
        self._load_model(model_dir=model_dir, graph_name=graph_name)

    def _load_model(self, model_dir, graph_name):
        CWD_PATH = os.getcwd()

        # Load model labels
        PATH_TO_LABELS = os.path.join(CWD_PATH, model_dir, 'labelmap.txt')
        with open(PATH_TO_LABELS, 'r') as f:
            labels = [line.strip() for line in f.readlines()]
        if labels[0] == '???':
            del(labels[0])
        self.labels = labels

        pkg = importlib.util.find_spec('tensorflow')
        if pkg is None:
            from tflite_runtime.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate
        else:
            from tensorflow.lite.python.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate

        # If using Edge TPU, assign filename for Edge TPU model
        if self.use_TPU:
            # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
            if (graph_name == 'cone_detect.tflite'):
                graph_name = 'cone_edgetpu.tflite'
        # Load the model 
        PATH_TO_CKPT = os.path.join(CWD_PATH, model_dir, graph_name)
        #self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        # Load the Tensorflow Lite model.
        # If using Edge TPU, use special load_delegate argument
        if self.use_TPU:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT,
                                    experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
            print(PATH_TO_CKPT)
        else:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        self.interpreter.allocate_tensors()

        # Get model details
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        self.height = self.input_details[0]['shape'][1]
        self.width = self.input_details[0]['shape'][2]
        self.floating_model = (self.input_details[0]['dtype'] == np.float32)
        self.input_mean = 127.5
        self.input_std = 127.5

    def classify(self, image_dir):
        images = glob.glob(image_dir + '/*.jpg')
        classes_list = []
        scores_list = []
        boxes_list =[]
        for image_path in images:
            print('Classifying: {}'.format(image_path))
            # Load image and resize to expected shape [1xHxWx3]
            image = cv2.imread(image_path)
            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            imH, imW, _ = image.shape 
            image_resized = cv2.resize(image_rgb, (self.width, self.height))
            input_data = np.expand_dims(image_resized, axis=0)

            # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
            if self.floating_model:
                input_data = (np.float32(input_data) - self.input_mean) / self.input_std

            # Perform the actual detection by running the model with the image as input
            self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
            self.interpreter.invoke()

            boxes = self.interpreter.get_tensor(self.output_details[0]['index'])[0] # Bounding box coordinates of detected objects
            classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
            scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects
            boxes_list.append(boxes[scores > self.min_conf_threshold])
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])

            # Get bounding box coordinates and draw box
            # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
            objects_dict ={}
            for i in range(len(scores)):
                if ((scores[i] > self.min_conf_threshold) and (scores[i] <= 1.0)):

                    ymin = int(max(1,(boxes[i][0] * imH)))
                    xmin = int(max(1,(boxes[i][1] * imW)))
                    ymax = int(min(imH,(boxes[i][2] * imH)))
                    xmax = int(min(imW,(boxes[i][3] * imW)))
                    print((xmin,ymin), (xmax,ymax),(imH,imW))
                    cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)

                    # Draw label
                    object_name = self.labels[int(classes[i])] # Look up object name from "labels" array using class index
                    objects_dict[object_name] = [(xmin,ymin), (xmax,ymax),(imH,imW)]
                    label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
                    labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
                    label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
                    cv2.rectangle(image, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
                    cv2.putText(image, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2) # Draw label text
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])
        objects_detected = {}
        for classes in classes_list:
            objects = set([self.labels[int(c)] for c in classes])
            for obj in objects:
                if obj in objects_detected.keys():
                    objects_detected[obj] += 1
                else:
                    objects_detected[obj] = 1
        return boxes_list, classes_list, scores_list, objects_detected, objects_dict


if __name__ == '__main__':

    model = ObjectClassificationModel('Sample_TFLite_model', '/home/pi/Pictures/scav_hunt')
    classes, scores, objects = model.classify(os.path.join(model.image_dir, 'archive/orange'))

import os
import glob
import picamera
import cv2
import numpy as np
import importlib.util
from datetime import datetime
import videorecorder as vr
import time
from collections import Counter

# If using TPU, need to load a different library
# from tensorflow.lite.python.interpreter import Interpreter


def take_picture(path):
    if path is None:
        path = "/home/pi/Pictures"
    camera = picamera.PiCamera()
    try:
        camera.capture(os.path.join(path, "image_{0}.jpg".format(datetime.now().strftime('%m%d%Y%H%M%S'))))
    finally:
        print('Picture taken')
        camera.close()

        
def record_video(path=None, cone_color='green', duration=5, runid=0):
    if path is None:
        path="/home/pi/Videos"
    path = os.path.join(path,cone_color)
    try:
        recorder = vr.VideoRecorder(path,runid)
        print('Loaded Video Recorder')
        recorder.start_recording()
        time.sleep(duration)
        recorder.stop_recording()
    except:
        print('Video Recording failed')
    finally:
        print('Video recorded')


class ObjectClassificationModel:

    def __init__(self, model_dir, image_dir, graph_name='detect.tflite', min_conf_threshold=0.5, use_TPU=False):

        self.model_dir = model_dir
        self.image_dir = image_dir
        self.min_conf_threshold = float(min_conf_threshold)
        self.use_TPU = use_TPU
        self._load_model(model_dir=model_dir, graph_name=graph_name)

    def _load_model(self, model_dir, graph_name):
        CWD_PATH = os.getcwd()

        # Load model labels
        PATH_TO_LABELS = os.path.join(CWD_PATH, model_dir, 'labelmap.txt')
        with open(PATH_TO_LABELS, 'r') as f:
            labels = [line.strip() for line in f.readlines()]
        if labels[0] == '???':
            del(labels[0])
        self.labels = labels

        pkg = importlib.util.find_spec('tensorflow')
        if pkg is None:
            from tflite_runtime.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate
        else:
            from tensorflow.lite.python.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate

        # If using Edge TPU, assign filename for Edge TPU model
        if self.use_TPU:
            # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
            if (graph_name == 'detect.tflite'):
                graph_name = 'edgetpu.tflite'

        PATH_TO_CKPT = os.path.join(CWD_PATH, model_dir, graph_name)
        
        # Load the Tensorflow Lite model.
        # If using Edge TPU, use special load_delegate argument
        if self.use_TPU:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT,
                                    experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
            print(PATH_TO_CKPT)
        else:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        self.interpreter.allocate_tensors()

        # Get model details
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        self.height = self.input_details[0]['shape'][1]
        self.width = self.input_details[0]['shape'][2]
        self.floating_model = (self.input_details[0]['dtype'] == np.float32)
        self.input_mean = 127.5
        self.input_std = 127.5

    def classify(self, image_dir):
        images = glob.glob(image_dir + '/*')
        classes_list = []
        scores_list = []
        for image_path in images:
            print('Classifying: {}'.format(image_path))
            
            # Load image and resize to expected shape [1xHxWx3]
            image = cv2.imread(image_path)
            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            imH, imW, _ = image.shape 
            image_resized = cv2.resize(image_rgb, (self.width, self.height))
            input_data = np.expand_dims(image_resized, axis=0)

            # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
            if self.floating_model:
                input_data = (np.float32(input_data) - self.input_mean) / self.input_std

            # Perform the actual detection by running the model with the image as input
            self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
            self.interpreter.invoke()

            # Retrieve detection results
            # We are not using the boxes right now since we do not need to know 
            # where picture the object is, only that it is there.
            # boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
            
            classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
            scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])

        objects_detected = {}
        for classes in classes_list:
            objects = set([self.labels[int(c)] for c in classes])
            for obj in objects:
                if obj in objects_detected.keys():
                    objects_detected[obj] += 1
                else:
                    objects_detected[obj] = 1

        return classes_list, scores_list, objects_detected
    
    
    def classify_video(self, video_dir):
        """Function to detect objects in video file"""
        #1. Get the list of all video files from the directory passed in
        videos = glob.glob(video_dir + '/*')

        #2. Check the number of *.avi files in the folder
        num_videos = len(videos)
        #3. Do not run classification if number of videos in the folder are more than 10 and alert
        if num_videos > 10:
            print('Found more than 10 videos in the directory: {}'.format(video_dir))
            return
        #4. For each video file
        for video_file in videos:
            video_name=os.path.basename(video_file)
            print('Processing video: {}'.format(video_name))
            #4.1 Open the video file 
            video = cv2.VideoCapture(video_file)
            imW = video.get(cv2.CAP_PROP_FRAME_WIDTH)
            imH = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
            collect_labels = []
            #4 .1.1 pass frame by frame to the  detection model
            index = 0
            print('Min Threshold',self.min_conf_threshold)
            while(video.isOpened()):
                # Acquire frame and resize to expected shape [1xHxWx3]
                ret, frame = video.read()
                if frame is None:
                    break
                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame_resized = cv2.resize(frame_rgb, (self.width, self.height))
                input_data = np.expand_dims(frame_resized, axis=0)
                
                # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
                if self.floating_model:
                    input_data = (np.float32(input_data) - self.input_mean) / self.input_std

                # Perform the actual detection by running the model with the image as input
                self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
                self.interpreter.invoke()

                # Retrieve detection results
                boxes = self.interpreter.get_tensor(self.output_details[0]['index'])[0] # Bounding box coordinates of detected objects
                classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
                scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects

                # Loop over all detections and draw detection box if confidence is above minimum threshold
                for i in range(len(scores)):
                    if ((scores[i] > self.min_conf_threshold) and (scores[i] <= 1.0)):

                        # Get bounding box coordinates and draw box
                        # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
                        ymin = int(max(1,(boxes[i][0] * imH)))
                        xmin = int(max(1,(boxes[i][1] * imW)))
                        ymax = int(min(imH,(boxes[i][2] * imH)))
                        xmax = int(min(imW,(boxes[i][3] * imW)))
                        
                        cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 4)

                        # Draw label
                        object_name = self.labels[int(classes[i])] # Look up object name from "labels" array using class index
                        label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
                        labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
                        label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
                        cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
                        cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
                        collect_labels.append(object_name)
                index += 1
                # All the results have been drawn on the frame, so it's time to display it.
                cv2.imshow('Object detector', frame)
            video.release()
            #cv2.destroyAllWindows()
            if len(collect_labels)>0:
                most_common,num_most_common = Counter(collect_labels).most_common(1)[0]
                max_object = most_common
            else:
                max_object = 'Nothing'
            print('Maximum detected object :{}'.format(max_object))
            print(Counter(collect_labels))
        return max_object


class ConeClassificationModel:
    def __init__(self, model_dir, image_dir, graph_name='cone_detect.tflite', min_conf_threshold=0.5, use_TPU=False):

        self.model_dir = model_dir
        self.image_dir = image_dir
        self.min_conf_threshold = float(min_conf_threshold)
        self.use_TPU = use_TPU
        self._load_model(model_dir=model_dir, graph_name=graph_name)

    def _load_model(self, model_dir, graph_name):
        CWD_PATH = os.getcwd()

        # Load model labels
        PATH_TO_LABELS = os.path.join(CWD_PATH, model_dir, 'labelmap.txt')
        with open(PATH_TO_LABELS, 'r') as f:
            labels = [line.strip() for line in f.readlines()]
        if labels[0] == '???':
            del(labels[0])
        self.labels = labels

        pkg = importlib.util.find_spec('tensorflow')
        if pkg is None:
            from tflite_runtime.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate
        else:
            from tensorflow.lite.python.interpreter import Interpreter
            if self.use_TPU:
                print('Loading tflite interpreter')
                from tflite_runtime.interpreter import load_delegate

        # If using Edge TPU, assign filename for Edge TPU model
        if self.use_TPU:
            # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
            if (graph_name == 'cone_detect.tflite'):
                graph_name = 'cone_edgetpu.tflite'
        # Load the model 
        PATH_TO_CKPT = os.path.join(CWD_PATH, model_dir, graph_name)
        #self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        # Load the Tensorflow Lite model.
        # If using Edge TPU, use special load_delegate argument
        if self.use_TPU:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT,
                                    experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
            print(PATH_TO_CKPT)
        else:
            self.interpreter = Interpreter(model_path=PATH_TO_CKPT)
        self.interpreter.allocate_tensors()

        # Get model details
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        self.height = self.input_details[0]['shape'][1]
        self.width = self.input_details[0]['shape'][2]
        self.floating_model = (self.input_details[0]['dtype'] == np.float32)
        self.input_mean = 127.5
        self.input_std = 127.5

    def classify(self, image_dir):
        images = glob.glob(image_dir + '/*.jpg')
        classes_list = []
        scores_list = []
        boxes_list =[]
        for image_path in images:
            print('Classifying: {}'.format(image_path))
            # Load image and resize to expected shape [1xHxWx3]
            image = cv2.imread(image_path)
            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            imH, imW, _ = image.shape 
            image_resized = cv2.resize(image_rgb, (self.width, self.height))
            input_data = np.expand_dims(image_resized, axis=0)

            # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
            if self.floating_model:
                input_data = (np.float32(input_data) - self.input_mean) / self.input_std

            # Perform the actual detection by running the model with the image as input
            self.interpreter.set_tensor(self.input_details[0]['index'],input_data)
            self.interpreter.invoke()

            boxes = self.interpreter.get_tensor(self.output_details[0]['index'])[0] # Bounding box coordinates of detected objects
            classes = self.interpreter.get_tensor(self.output_details[1]['index'])[0] # Class index of detected objects
            scores = self.interpreter.get_tensor(self.output_details[2]['index'])[0] # Confidence of detected objects
            boxes_list.append(boxes[scores > self.min_conf_threshold])
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])

            # Get bounding box coordinates and draw box
            # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
            objects_dict ={}
            for i in range(len(scores)):
                if ((scores[i] > self.min_conf_threshold) and (scores[i] <= 1.0)):

                    ymin = int(max(1,(boxes[i][0] * imH)))
                    xmin = int(max(1,(boxes[i][1] * imW)))
                    ymax = int(min(imH,(boxes[i][2] * imH)))
                    xmax = int(min(imW,(boxes[i][3] * imW)))
                    print((xmin,ymin), (xmax,ymax),(imH,imW))
                    cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)

                    # Draw label
                    object_name = self.labels[int(classes[i])] # Look up object name from "labels" array using class index
                    objects_dict[object_name] = [(xmin,ymin), (xmax,ymax),(imH,imW)]
                    label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
                    labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
                    label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
                    cv2.rectangle(image, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
                    cv2.putText(image, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2) # Draw label text
            classes_list.append(classes[scores > self.min_conf_threshold])
            scores_list.append(scores[scores > self.min_conf_threshold])
        objects_detected = {}
        for classes in classes_list:
            objects = set([self.labels[int(c)] for c in classes])
            for obj in objects:
                if obj in objects_detected.keys():
                    objects_detected[obj] += 1
                else:
                    objects_detected[obj] = 1
        return boxes_list, classes_list, scores_list, objects_detected, objects_dict


if __name__ == '__main__':

    model = ObjectClassificationModel('Sample_TFLite_model', '/home/pi/Pictures/scav_hunt')
    classes, scores, objects = model.classify(os.path.join(model.image_dir, 'archive/orange'))

# If using TPU, need to load a different library # from tensorflow.lite.python.interpreter import Interpreter # Load model labels # If using Edge TPU, assign filename for Edge TPU model # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite' # Load the Tensorflow Lite model. # If using Edge TPU, use special load_delegate argument # Get model details # Load image and resize to expected shape [1xHxWx3] # Normalize pixel values if using a floating model (i.e. if model is non-quantized) # Perform the actual detection by running the model with the image as input # Retrieve detection results # We are not using the boxes right now since we do not need to know # where picture the object is, only that it is there. # boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects # Class index of detected objects # Confidence of detected objects Function to detect objects in video file #1. Get the list of all video files from the directory passed in #2. Check the number of *.avi files in the folder #3. Do not run classification if number of videos in the folder are more than 10 and alert #4. For each video file #4.1 Open the video file #4 .1.1 pass frame by frame to the  detection model # Acquire frame and resize to expected shape [1xHxWx3] # Normalize pixel values if using a floating model (i.e. if model is non-quantized) # Perform the actual detection by running the model with the image as input # Retrieve detection results # Bounding box coordinates of detected objects # Class index of detected objects # Confidence of detected objects # Loop over all detections and draw detection box if confidence is above minimum threshold # Get bounding box coordinates and draw box # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min() # Draw label # Look up object name from "labels" array using class index # Example: 'person: 72%' # Get font size # Make sure not to draw label too close to top of window # Draw white box to put label text in # Draw label text # All the results have been drawn on the frame, so it's time to display it. #cv2.destroyAllWindows() # Load model labels # If using Edge TPU, assign filename for Edge TPU model # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite' # Load the model #self.interpreter = Interpreter(model_path=PATH_TO_CKPT) # Load the Tensorflow Lite model. # If using Edge TPU, use special load_delegate argument # Get model details # Load image and resize to expected shape [1xHxWx3] # Normalize pixel values if using a floating model (i.e. if model is non-quantized) # Perform the actual detection by running the model with the image as input # Bounding box coordinates of detected objects # Class index of detected objects # Confidence of detected objects # Get bounding box coordinates and draw box # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min() # Draw label # Look up object name from "labels" array using class index # Example: 'person: 72%' # Get font size # Make sure not to draw label too close to top of window # Draw white box to put label text in # Draw label text