yolo.py

# -*- 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 random
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
from kalman_filter import KalmanFilter
from tracker import Tracker
import multiprocessing as mpr
from datetime import datetime

class YOLO(object):
    _defaults = {
        "model_path": 'model_data/yolo-tiny.h5',
        "anchors_path": 'model_data/tiny_yolo_anchors.txt',
        "classes_path": 'model_data/coco_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)
        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'))

        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
        box_motorbike = []
        box_car = []
        for i, c in reversed(list(enumerate(out_classes))):
            
            predicted_class = self.class_names[c]
            if predicted_class=='car' or predicted_class == 'motorbike':
                
                box = out_boxes[i]
                score = out_scores[i]
                if predicted_class == 'car':
                    if score >0.4:
                      box_car.append(box)
                else:
                    box_motorbike.append(box)
                

                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
            else:
                out_boxes = np.delete(out_boxes,i,0)

        end = timer()
      #  print(end - start)
        print(out_boxes)
        return image,box_car,box_motorbike

    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()
    number = 0
    centers = [] 

	# y-cooridinate for speed detection line
    Y_THRESH = 160
    font = cv2.FONT_HERSHEY_PLAIN
    blob_min_width_far = 50
    blob_min_height_far = 50

    blob_min_width_near = 50
    blob_min_height_near = 50
    tracker_motorbike = Tracker(100, 12, 12, 1)
    tracker_car = Tracker(80, 3, 2, 1)
    

    while True:
        car_centers = []
        bike_centers = []
        return_value, frame = vid.read()
        image = Image.fromarray(frame)
        image,box_car,box_motorbike = 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)

        for box in box_car:
            y, x, y2, x2 = box
            h = y2 - y
            w = x2 - x
            if y > Y_THRESH:
                if w >= blob_min_width_near and h >= blob_min_height_near:
                    center = np.array ([[x+w/2], [y+h/2]])
                    car_centers.append(np.round(center))
                    cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)
            else:
                if w >= blob_min_width_far and h >= blob_min_height_far:
                    center = np.array ([[x+w/2], [y+h/2]])
                    car_centers.append(np.round(center))
                    cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)
        if car_centers:
            tracker_car.update(car_centers)
        
        for box in box_motorbike:
            y, x, y2, x2 = box
            h = y2 - y
            w = x2 - x
            if y > Y_THRESH:
                if w >= blob_min_width_near and h >= blob_min_height_near:
                    center = np.array ([[x+w/2], [y+h/2]])
                    bike_centers.append(np.round(center))
                    cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)
            else:
                if w >= blob_min_width_far and h >= blob_min_height_far:
                    center = np.array ([[x+w/2], [y+h/2]])
                    bike_centers.append(np.round(center))
                    cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)
        if bike_centers:
            tracker_motorbike.update(bike_centers)

        number_bike = tracker_motorbike.trackIdCount
        number_car = tracker_car.trackIdCount
        cv2.putText(result, text='Bike:'+str(number_bike), org=(500, 50), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=2, color=(0, 0, 255), thickness=2)
        cv2.putText(result, text='Car:'+str(number_car), org=(500, 100), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=2, color=(0, 0, 255), 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

        cv2.imwrite('/home/vietphan/Documents/keras-yolo3-master/img/'+str(number) + ".jpg", result)
        number +=1
    yolo.close_session()