※Python初心者ですご容赦ください  
※OSはWindows10です

keras-yolo3  
<https://github.com/qqwweee/keras-yolo3>  
というライブラリがあり，  
その中にはyolo_video.py , yolo.py というpythonコードが入っています．

事前にmodel_dataフォルダ内に学習済のモデルを置き，ターミナル上で

```

    python yolo_video.py --input 画像ファイル名.jpg 
    
```

みたいに入力すると，例えば入力した画像内から本を検出してくれるといった具合です．  
(自分のPCでも正しく動作することを確認しました)

[![画像はその一例です](https://i.stack.imgur.com/rjC9p.png)](https://i.stack.imgur.com/rjC9p.png)

yolo.pyのソースコードがこちらです．

```

    # -*- 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/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
    
            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()
    
```

yolo_video.pyのソースコードがこちらです．

```

    import sys
    import argparse
    from yolo import YOLO, detect_video
    from PIL import Image
    
    def detect_img(yolo):
        while True:
            img = input('Input image filename:')
            try:
                image = Image.open(img)
            except:
                print('Open Error! Try again!')
                continue
            else:
                r_image = yolo.detect_image(image)
                r_image.show()
        yolo.close_session()
    
    FLAGS = None
    
    if __name__ == '__main__':
        # class YOLO defines the default value, so suppress any default here
        parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS)
        '''
        Command line options
        '''
        parser.add_argument(
            '--model', type=str,
            help='path to model weight file, default ' + YOLO.get_defaults("model_path")
        )
    
        parser.add_argument(
            '--anchors', type=str,
            help='path to anchor definitions, default ' + YOLO.get_defaults("anchors_path")
        )
    
        parser.add_argument(
            '--classes', type=str,
            help='path to class definitions, default ' + YOLO.get_defaults("classes_path")
        )
    
        parser.add_argument(
            '--gpu_num', type=int,
            help='Number of GPU to use, default ' + str(YOLO.get_defaults("gpu_num"))
        )
    
        parser.add_argument(
            '--image', default=False, action="store_true",
            help='Image detection mode, will ignore all positional arguments'
        )
        '''
        Command line positional arguments -- for video detection mode
        '''
        parser.add_argument(
            "--input", nargs='?', type=str,required=False,default='./path2your_video',
            help = "Video input path"
        )
    
        parser.add_argument(
            "--output", nargs='?', type=str, default="",
            help = "[Optional] Video output path"
        )
    
        FLAGS = parser.parse_args()
    
        if FLAGS.image:
            """
            Image detection mode, disregard any remaining command line arguments
            """
            print("Image detection mode")
            if "input" in FLAGS:
                print(" Ignoring remaining command line arguments: " + FLAGS.input + "," + FLAGS.output)
            detect_img(YOLO(**vars(FLAGS)))
        elif "input" in FLAGS:
            detect_video(YOLO(**vars(FLAGS)), FLAGS.input, FLAGS.output)
        else:
            print("Must specify at least video_input_path.  See usage with --help.")
    
```

yolo_video.py を実行すると，yolo.py が呼び出されるような仕組みになっています．

今回自前のソースコード sample.py で yolo.py を呼び出し，  
リアルタイムに物体検出をしたいと考えています(yolo_video.pyを使わない)．  
sample.py のソースコードがこちらになります(関係ない部分は省略しています)．

```

    import sys
    import argparse
    import cv2
    from yolo import YOLO, detect_video
    from PIL import Image
    ↑ yolo_video.pyでインポートされていたモジュールをコピーしてきました
    
    image = camera.get_image()  ← imageにカメラで撮影された画像が入ります
    
    image_size = add_margin(rescale(image),SIZE) ← yoloの性質上，画像は正方形である必要があるのでSIZE×SIZEの正方形画像に変換しています
    
    nose_data = yolo.detect_image(image_size_changed) ← yolo.pyのdetect_image()を呼び出し，人の顔から鼻を検出し，検出した領域の座標をnose_dataに入れたいです
    
```

このようなプログラムを作成したのですが，実行時に以下のエラーが発生しました．

```

    nose_data = yolo.detect_image(image_size)
    NameError: name 'yolo' is not defined
    
```

yolo_video.pyのどれが必要でどれが必要ないのか分かりません．  
もしよければアドバイスいただけないでしょうか．
CC BY-SA 4.0
2021-06-03T08:59:31.603
76304
2021-06-04T09:41:57.907
41740
question
python
yolo.py を呼び出してリアルタイムに検出したい
detect_imageはクラスYOLO内で定義されている関数だったので  
nose_data = YOLO.detect_image(image_size) でいけました

detect_videoはクラスYOLO外で定義されていたので，  
もし使用するなら  
nose_data = detect_video(image_size) になりそうです

いずれにせよコンパイルは通りましたこと報告いたします．
CC BY-SA 4.0
2021-06-04T09:41:57.907
77336
2021-06-04T09:41:57.907
41740
76304
answer