在實習的期間為公司寫的紅綠燈檢測，基于YOLOv3的訓練好的權重，不需要自己重新訓練，只需要調用yolov3.weights，可以做到視頻或圖片中紅綠燈的檢測識別。

自動檢測識別效果

1.紅燈檢測

2.綠燈檢測

python源碼

"""
Class definition of YOLO_v3 style detection model on image and video
"""

import colorsys
import os
from timeit import default_timer as timer
import cv2
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
import collections


class YOLO(object):
  _defaults = {
    "model_path": ''model_data/yolo.h5'',
    "anchors_path": ''model_data/yolo_anchors.txt'',
    "classes_path": ''model_data/coco_classes.txt'',
    "score" : 0.3,
    "iou" : 0.35,
    "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_, self.input_image_shape,
        score_threshold=self.score, iou_threshold=self.iou)
    return boxes, scores, classes

  def getColorList(self):
    dict = collections.defaultdict(list)

    # 紅色
    lower_red = np.array([156, 43, 46])
    upper_red = np.array([180, 255, 255])
    color_list = []
    color_list.append(lower_red)
    color_list.append(upper_red)
    dict[''red''] = color_list

    # 紅色2
    lower_red = np.array([0, 43, 46])
    upper_red = np.array([10, 255, 255])
    color_list = []
    color_list.append(lower_red)
    color_list.append(upper_red)
    dict[''red2''] = color_list

    # 橙色
    lower_orange = np.array([11, 43, 46])
    upper_orange = np.array([25, 255, 255])
    color_list = []
    color_list.append(lower_orange)
    color_list.append(upper_orange)
    dict[''orange''] = color_list

    # 黃色
    lower_yellow = np.array([26, 43, 46])
    upper_yellow = np.array([34, 255, 255])
    color_list = []
    color_list.append(lower_yellow)
    color_list.append(upper_yellow)
    dict[''yellow''] = color_list

    # 綠色
    lower_green = np.array([35, 43, 46])
    upper_green = np.array([77, 255, 255])
    color_list = []
    color_list.append(lower_green)
    color_list.append(upper_green)
    dict[''green''] = color_list
    return dict

  def get_color(self,frame):
    print(''go in get_color'')
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    maxsum = -100
    color = None
    color_dict = self.getColorList()
    score = 0
    type = ''black''
    for d in color_dict:
      mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
      # print(cv2.inRange(hsv, color_dict[d][0], color_dict[d][1]))
      #cv2.imwrite(''images/triffic/'' + f + d + ''.jpg'', mask)
      binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
      binary = cv2.dilate(binary, None, iterations=2)
      img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
      sum = 0
      for c in cnts:
        sum += cv2.contourArea(c)

      if sum > maxsum:
        maxsum = sum
        color = d
      if sum > score:
        score = sum
        type = d
    return type



  def detect_image(self, image,path):
    print(''class'',self._get_class())

    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
    thickness = 5
    print(''thickness'',thickness)
    print(''out_classes'',out_classes)
    my_class = [''traffic light'']
    imgcv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)

    for i, c in reversed(list(enumerate(out_classes))):
      predicted_class = self.class_names[c]
      print(''predicted_class'',predicted_class)
      if predicted_class not in my_class:
        continue
      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))
      img2 = imgcv[top:bottom, left:right]
      color = self.get_color(img2)
      cv2.imwrite(''images/triffic/''+path+str(i) + ''.jpg'', img2)
      if color== ''red'' or color == ''red2'':
        cv2.rectangle(imgcv, (left, top), (right, bottom), color=(0, 0, 255),
               lineType=2, thickness=8)
        cv2.putText(imgcv, ''{0} {1:.2f}''.format(color, score),
              (left, top - 15),
              cv2.FONT_HERSHEY_SIMPLEX,
              1.2, (0, 0, 255), 4,
              cv2.LINE_AA)
      elif color == ''green'':
        cv2.rectangle(imgcv, (left, top), (right, bottom), color=(0, 255, 0),
               lineType=2, thickness=8)
        cv2.putText(imgcv, ''{0} {1:.2f}''.format(color, score),
              (left, top - 15),
              cv2.FONT_HERSHEY_SIMPLEX,
              1.2, (0, 255, 0), 4,
              cv2.LINE_AA)
      else:
        cv2.rectangle(imgcv, (left, top), (right, bottom), color=(255, 0, 0),
               lineType=2, thickness=8)
        cv2.putText(imgcv, ''{0} {1:.2f}''.format(color, score),
              (left, top - 15),
              cv2.FONT_HERSHEY_SIMPLEX,
              1.2, (255, 0, 0), 4,
              cv2.LINE_AA)

      print(imgcv.shape)
    end = timer()
    print(end - start)
    return imgcv

  def close_session(self):
    self.sess.close()

def detect_img(yolo, img_path,fname):
  img = Image.open(img_path)
  import time
  t1 = time.time()

  img = yolo.detect_image(img,fname)
  print(''time: {}''.format(time.time() - t1))
  return img
  #yolo.close_session()

if __name__ == ''__main__'':
  yolo = YOLO()
  video_full_path = ''images/triffic.mp4''
  output = ''images/res.avi''
  cap = cv2.VideoCapture(video_full_path)
  cap.set(cv2.CAP_PROP_POS_FRAMES, 1) # 設置要獲取的幀號

  fourcc = cv2.VideoWriter_fourcc(*''XVID'')
  fps = cap.get(cv2.CAP_PROP_FPS)
  size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))

  out = cv2.VideoWriter(output, fourcc, fps, size)
  ret = True
  count = 0
  while ret :
    count+=1
    ret, frame = cap.read()
    if not ret :
      print(''結束'')
      break
    image = Image.fromarray(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB))
    image = yolo.detect_image(image,''pic'')
    out.write(image)

  cap.release()
  out.release()
  cv2.destroyAllWindows()