手把手教你用深度学习做物体检测(一)：快速感受物体检测的酷炫

佚名 5年前 (2019-09-02) 人工智能 595人围观抢沙发百度已收录

我们先来看看什么是物体检测，见下图：

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如上图所示，物体检测就是需要检测出图像中有哪些目标物体，并且框出其在图像中的位置。

本篇文章，我将会介绍如何利用训练好的物体检测模型来快速实现上图的效果，这里我们将会用到基于coco数据集训练的yolov3模型，该模型能识别80类物品，具体如下：

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双肩包 雨伞 手提包 领带 手提箱 飞盘 双架滑雪板 滑雪板 球 风筝 棒球棍 棒球手套 滑板 冲浪板 网球拍 瓶子 酒杯 杯子 叉子 刀 勺子 碗 
香蕉 苹果 三明治 橙子 西兰花 胡萝卜 热狗 披萨 炸面圈 蛋糕 椅子 沙发 盆栽 床 餐桌 厕所 显示器 笔记本电脑 鼠标 遥控器 键盘 手机 
微波炉 电烤箱 烤面包器 水槽 冰箱 书 钟 花瓶 剪刀 泰迪熊 吹风机 牙刷

下面，我们来看具体如何实现。第一步：从github上下载项目： https://github.com/qqwweee/keras-yolo3 该项目是基于keras的yolov3实现，keras是一个深度学习高层框架，提供了更友好的接口，其底层可以兼容很多深度学习框架，比如tensorflow等。yolo是目前很流行的物体检测算法，yolov3是第三个版本，也是最新的版本。第二步：安装keras。通过pip安装即可，如果后续有遇到本地环境没有的包，也通过pip安装就好了（这里假设你已经装好了python的相关环境，并且知道如何使用pip，如果你还不清楚，可以自行网上搜索，过程也不复杂）。第三步：下载yolov3.weights，这个文件是darknet预训练好的yolov3模型，可以检测coco数据集中涵盖的80类物体。地址：https://pjreddie.com/media/files/yolov3.weights 第四步：执行以下命令，将下载下来的文件转换为keras可以使用的.h5模型文件

python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

第五步：将项目中的yolo.py,用下面代码替换，注意检查_defaults中的配置路径是否正确

# -*- 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 PIL import Image, ImageFont, ImageDraw from keras import backend as K from keras.layers import Input from keras.models import load_model from keras.utils import multi_gpu_model from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body from yolo3.utils import letterbox_image class YOLO(object): _defaults = { "model_path": 'model_data/yolo_weights.h5', "anchors_path": 'model_data/yolo_anchors.txt', "classes_path": 'model_data/coco_classes.txt', "score" : 0.3, "iou" : 0.45, "model_image_size" : (416, 416), "gpu_num" : 0, } @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, encoding="utf-8") 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, encoding="utf-8") 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'))
               # 使用中文字体
        font = ImageFont.truetype(font='font/simfang.ttf', 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_FourCC = cv2.VideoWriter_fourcc(*"mp4v") 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() if return_value: 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("Object Detect", cv2.WINDOW_NORMAL) cv2.resizeWindow("Object Detect", 640, 480); cv2.imshow("Object Detect", result) if isOutput: print("start write...==========================================================================") out.write(result) if cv2.waitKey(1) & 0xFF == ord('q'): break
        else: break out.release() vid.release() cv2.destroyAllWindows() yolo.close_session() def for_img(yolo): path = 'images/IMG_0728.JPG'
    try: image = Image.open(path) except: print('Open Error! Try again!') else: r_image = yolo.detect_image(image) r_image.show() yolo.close_session() def for_video(yolo): detect_video(yolo, "videos/xx.mp4", "videos/xx_detect.mp4") if __name__ == '__main__': _yolo = YOLO() for_img(_yolo) # for_video(_yolo)

本项目是可以在CPU上运行的，但是GPU上运行的更快。关于如何搭建GPU的运行环境，感兴趣的读者可以参考《如何在阿里云租一台GPU服务器做深度学习？》，然后将上面的代码的gpu_num改为你的GPU号(可以使用nvidia-smi命令查看)，并注意加入对GPU显存的使用控制即可，这里为了快速体验物体检测效果，就不再对GPU下运行程序做过多的介绍，虽然在CPU下运行会慢很多，但用于体验足够了。做完上面的步骤后，执行yolo.py，将会看到你想检测的图像的物体检测效果，左边是原图，该图项目中是没有的，可以自行下载，或者用你喜欢的其它图片来尝试检测：手把手教你用深度学习做物体检测(一)：快速感受物体检测的酷炫人工智能第2张