--- a
+++ b/YOLO/yolo.py
@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+"""
+Class definition of YOLO_v3 style detection model on image and video
+"""
+
+import colorsys
+import os
+from timeit import default_timer as timer
+
+import numpy as np
+from keras import backend as K
+from keras.models import load_model
+from keras.layers import Input
+from PIL import Image, ImageFont, ImageDraw
+
+from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
+from yolo3.utils import letterbox_image
+import os
+from keras.utils import multi_gpu_model
+
+class YOLO(object):
+    _defaults = {
+        "model_path": 'model_data/yolo.h5',
+        "anchors_path": 'model_data/yolo_anchors.txt',
+        "classes_path": 'model_data/med_classes.txt',
+        "score" : 0.3,
+        "iou" : 0.45,
+        "model_image_size" : (416, 416),
+        "gpu_num" : 1,
+    }
+
+    @classmethod
+    def get_defaults(cls, n):
+        if n in cls._defaults:
+            return cls._defaults[n]
+        else:
+            return "Unrecognized attribute name '" + n + "'"
+
+    def __init__(self, **kwargs):
+        self.__dict__.update(self._defaults) # set up default values
+        self.__dict__.update(kwargs) # and update with user overrides
+        self.class_names = self._get_class()
+        self.anchors = self._get_anchors()
+        self.sess = K.get_session()
+        self.boxes, self.scores, self.classes = self.generate()
+
+    def _get_class(self):
+        classes_path = os.path.expanduser(self.classes_path)
+        with open(classes_path) as f:
+            class_names = f.readlines()
+        class_names = [c.strip() for c in class_names]
+        return class_names
+
+    def _get_anchors(self):
+        anchors_path = os.path.expanduser(self.anchors_path)
+        with open(anchors_path) as f:
+            anchors = f.readline()
+        anchors = [float(x) for x in anchors.split(',')]
+        return np.array(anchors).reshape(-1, 2)
+
+    def generate(self):
+        model_path = os.path.expanduser(self.model_path)
+        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
+
+        # Load model, or construct model and load weights.
+        num_anchors = len(self.anchors)
+        num_classes = len(self.class_names)
+        is_tiny_version = num_anchors==6 # default setting
+        try:
+            self.yolo_model = load_model(model_path, compile=False)
+            print(self.yolo_model.layers[-1].output_shape[-1])
+        except:
+            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
+                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
+            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
+        else:
+            assert self.yolo_model.layers[-1].output_shape[-1] == \
+                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
+                'Mismatch between model and given anchor and class sizes'
+
+        print('{} model, anchors, and classes loaded.'.format(model_path))
+
+        # Generate colors for drawing bounding boxes.
+        hsv_tuples = [(x / len(self.class_names), 1., 1.)
+                      for x in range(len(self.class_names))]
+        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+        self.colors = list(
+            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
+                self.colors))
+        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
+        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
+        np.random.seed(None)  # Reset seed to default.
+
+        # Generate output tensor targets for filtered bounding boxes.
+        self.input_image_shape = K.placeholder(shape=(2, ))
+        if self.gpu_num>=2:
+            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
+        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
+                len(self.class_names), self.input_image_shape,
+                score_threshold=self.score, iou_threshold=self.iou)
+        return boxes, scores, classes
+
+    def detect_image(self, image):
+        start = timer()
+
+        if self.model_image_size != (None, None):
+            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
+            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
+            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
+        else:
+            new_image_size = (image.width - (image.width % 32),
+                              image.height - (image.height % 32))
+            boxed_image = letterbox_image(image, new_image_size)
+        image_data = np.array(boxed_image, dtype='float32')
+
+        print(image_data.shape)
+        image_data /= 255.
+        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.
+
+        out_boxes, out_scores, out_classes = self.sess.run(
+            [self.boxes, self.scores, self.classes],
+            feed_dict={
+                self.yolo_model.input: image_data,
+                self.input_image_shape: [image.size[1], image.size[0]],
+                K.learning_phase(): 0
+            })
+
+        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
+        print(out_boxes)
+
+        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
+                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
+        thickness = (image.size[0] + image.size[1]) // 300
+
+        for i, c in reversed(list(enumerate(out_classes))):
+            predicted_class = self.class_names[c]
+            box = out_boxes[i]
+            score = out_scores[i]
+
+            label = '{} {:.2f}'.format(predicted_class, score)
+            draw = ImageDraw.Draw(image)
+            label_size = draw.textsize(label, font)
+
+            top, left, bottom, right = box
+            top = max(0, np.floor(top + 0.5).astype('int32'))
+            left = max(0, np.floor(left + 0.5).astype('int32'))
+            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
+            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
+            print(label, (left, top), (right, bottom))
+
+            if top - label_size[1] >= 0:
+                text_origin = np.array([left, top - label_size[1]])
+            else:
+                text_origin = np.array([left, top + 1])
+
+            # My kingdom for a good redistributable image drawing library.
+            for i in range(thickness):
+                draw.rectangle(
+                    [left + i, top + i, right - i, bottom - i],
+                    outline=self.colors[c])
+            draw.rectangle(
+                [tuple(text_origin), tuple(text_origin + label_size)],
+                fill=self.colors[c])
+            draw.text(text_origin, label, fill=(0, 0, 0), font=font)
+            del draw
+
+        end = timer()
+        print(end - start)
+        return image
+
+    def close_session(self):
+        self.sess.close()
+
+def detect_video(yolo, video_path, output_path=""):
+    import cv2
+    vid = cv2.VideoCapture(video_path)
+    if not vid.isOpened():
+        raise IOError("Couldn't open webcam or video")
+    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))
+    video_fps       = vid.get(cv2.CAP_PROP_FPS)
+    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+    isOutput = True if output_path != "" else False
+    if isOutput:
+        print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
+        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
+    accum_time = 0
+    curr_fps = 0
+    fps = "FPS: ??"
+    prev_time = timer()
+    while True:
+        return_value, frame = vid.read()
+        image = Image.fromarray(frame)
+        image = yolo.detect_image(image)
+        result = np.asarray(image)
+        curr_time = timer()
+        exec_time = curr_time - prev_time
+        prev_time = curr_time
+        accum_time = accum_time + exec_time
+        curr_fps = curr_fps + 1
+        if accum_time > 1:
+            accum_time = accum_time - 1
+            fps = "FPS: " + str(curr_fps)
+            curr_fps = 0
+        cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+                    fontScale=0.50, color=(255, 0, 0), thickness=2)
+        cv2.namedWindow("result", cv2.WINDOW_NORMAL)
+        cv2.imshow("result", result)
+        if isOutput:
+            out.write(result)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    yolo.close_session()