編集履歴

質問編集履歴

コードの編集

2020/08/24 02:54

投稿

gurou

スコア8

test CHANGED Viewed

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@@ -12,175 +12,431 @@
 現在は以下のコードで動画検出を行っています
+```python# -*- coding: utf-8 -*-
+"""
+Class definition of YOLO_v3 style detection model on image and video
+"""
-```python
+import colorsys
-import sys
+import os
-import argparse
+from timeit import default_timer as timer
 import numpy as np
-from yolo import YOLO, detect_video
-from PIL import Image
-def detect_img(yolo):
+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": 'C:/Users/0000526465/keras-yolo3/logs/000/trained_weights_final.h5',
+        "anchors_path": 'model_data/yolo_anchors.txt',
+        "classes_path": 'model_data/my_classes.txt',
+        "score" : 0.3,
+        "iou" : 0.45,
+        "model_image_size" : (320,320),
+        "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:
-        img = input('Input image filename:')
+        return_value, frame = vid.read()
-        try:
-            image = Image.open(img)
+        image = Image.fromarray(frame)
-        except:
-            print('Open Error! Try again!')
-            continue
-        else:
-            r_image = yolo.detect_image(image)
+        image = yolo.detect_image(image)
-            print(type(r_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)
-            import cv2
+            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.imwrite("out.jpg", np.asarray(r_image)[..., ::-1])
+        cv2.namedWindow("result", cv2.WINDOW_NORMAL)
+        cv2.imshow("result", result)
+        if isOutput:
-            r_image.show()
+            out.write(result)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
     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.")

コードの修正

2020/08/24 02:54

投稿

gurou

スコア8

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -11,8 +11,6 @@
 現在は以下のコードで動画検出を行っています
 ```python
@@ -183,3 +181,7 @@
     else:
         print("Must specify at least video_input_path.  See usage with --help.")
+```

コードの追加

2020/08/20 06:12

投稿

gurou

スコア8

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -7,3 +7,179 @@
 なんとなくフレームレートの事をさしているということはわかりましたが
 １１とは良いのでしょうか。悪いのでしょうか。
+現在は以下のコードで動画検出を行っています
+```python
+import sys
+import argparse
+import numpy as np
+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)
+            print(type(r_image))
+            import cv2
+            cv2.imwrite("out.jpg", np.asarray(r_image)[..., ::-1])
+            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.")