python 智能识别 超市商品 python人工智能 图像识别可以检测图片，视频流，有界面_商品识别与检测系统

随着社会经济的发展，选择到超市购物的消费者越来越多，超市排长队付账的矛盾也越来越突出。对此，我们提出一种新型的购物车，通过识别商品录入同时放入购物车中，并利用检测系统检测是否与已知的商品信息相匹配，并把商品信息传送到显示屏上。实现人工智能识别超市商品商品

import colorsys
import os
from timeit import default_timer as timer
import time
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
import cv2
from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
 
 
class YOLO(object):
    def __init__(self):
        self.model_path = 'goods-train/yolov3.h5'  # model path or trained weights path
        self.anchors_path = 'model_data/yolo_anchors.txt'
        self.classes_path = 'model_data/goods_classes.txt'
 
        self.score = 0.4
        self.iou = 0.45
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.model_image_size = (416, 416)  # fixed size or (None, None), hw
        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,))
        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):
        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.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
            })
        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))
            center_y = (top + bottom) / 2
            center_x = (left + right) / 2
            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.point((center_x, center_y), fill=(255, 0, 0))
            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
        return image, len(out_classes)
 
    def close_session(self):
        self.sess.close()
 
 
def detect_img(yolo):
    traindata_path = 'images'
    num_count = 0
    for img in os.listdir(traindata_path):
        print(img)
        try:
            image = Image.open(traindata_path + '/' + img)
            print("单幅图像", image.mode)
        except:
            print('Open Error! Try again!')
        else:
            r_image, num = yolo.detect_image(image)
            num_count = num_count + num
            result = np.asarray(r_image)
            result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
            cv2.imwrite('result/' + img, result)
            # r_image.show()
    yolo.close_session()
    print('-----------------')
    print('total images:%s' % len(os.listdir(traindata_path)))
    print('detect num:%s' % num_count)
 
if __name__ == '__main__':
    detect_img(YOLO())

效果图：

效果视频：

项目代码下载：

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