在实习的期间为公司写的红绿灯检测，基于YOLOv3的训练好的权重，不需要自己重新训练，只需要调用yolov3.weights，可以做到视频或图片中红绿灯的检测识别。
自动检测识别效果
1.灯检测
2.绿灯检测
python源码
"""Class definition of YOLO_v3 style detection model on image and video"""import colorsysimport osfrom timeit import default_timer as timerimport cv2import numpy as npfrom keras import backend as Kfrom keras.models import load_modelfrom keras.layers import Inputfrom PIL import Image, ImageFont, ImageDrawfrom yolo3.model import yolo_eval, yolo_body, tiny_yolo_bodyfrom yolo3.utils import letterbox_imageimport osfrom keras.utils import multi_gpu_modelimport collectionsclass 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) # 红色 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 # 红色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 # 黄色 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 # 绿色 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) # 设置要获取的帧号 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(''结束'') 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()