Tensorflow2.0 实现 YOLOv3（九）：image_demo.py

文章说明

本系列文章旨在对 Github 上 malin9402 提供的代码进行说明，在这篇文章中，我们会对 YOLOv3 项目中的 image_demo.py 文件进行说明。这个程序的作用是对输入的图片中的内容进行目标检测，并在图上标注检测框。

如果只是想运行 Github 上的代码，可以参考对 YOLOv3 代码的说明一文。

导入需要的库

import cv2
import numpy as np
import core.utils as utils
import tensorflow as tf
from core.yolov3 import YOLOv3, decode
from PIL import Image

导入并处理图片

input_size   = 416  # 输入模型的图片尺寸
image_path   = "./docs/kite.jpg"  # 图片路径original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)  # original_image.shape=(900, 1352, 3)original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])  # (416, 416, 3)image_data = image_data[np.newaxis, ...].astype(np.float32)  # (1, 416, 416, 3)

构造模型

# 得到三层特征图
input_layer  = tf.keras.layers.Input([input_size, input_size, 3])
feature_maps = YOLOv3(input_layer)# 将特征图解码
bbox_tensors = []
for i, fm in enumerate(feature_maps):bbox_tensor = decode(fm, i)bbox_tensors.append(bbox_tensor)# 构建模型
model = tf.keras.Model(input_layer, bbox_tensors)# 将已经训练好的权值赋给模型
utils.load_weights(model, "./yolov3.weights")

得到 feature map 上的预测框

# 将图片输入模型得到预测框
# pred_bbox 是个列表，其中含有三个数组
# 数组的形状分别是：(1, 52, 52, 3, 15)，(1, 26, 26, 3, 15)，(1, 13, 13, 3, 15)
pred_bbox = model.predict(image_data)  
# 将每个数组形状转换为（-1, 15）
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
# 把所有框的信息整合起来
pred_bbox = tf.concat(pred_bbox, axis=0)

将 feature map 上的预测框信息转换到原始图像上

bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3)

极大值抑制

bboxes = utils.nms(bboxes, 0.45, method='nms')

在原图上画框并标注类别信息

image = utils.draw_bbox(original_image, bboxes)

展示图片

image = Image.fromarray(image)
image.show()

完整代码

import cv2
import numpy as np
import core.utils as utils
import tensorflow as tf
from core.yolov3 import YOLOv3, decode
from PIL import Imageinput_size   = 416
image_path   = "./docs/kite.jpg"input_layer  = tf.keras.layers.Input([input_size, input_size, 3])
feature_maps = YOLOv3(input_layer)original_image      = cv2.imread(image_path)
original_image      = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)bbox_tensors = []
for i, fm in enumerate(feature_maps):bbox_tensor = decode(fm, i)bbox_tensors.append(bbox_tensor)model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, "./yolov3/yolov3.weights")
model.summary()pred_bbox = model.predict(image_data)
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3)
bboxes = utils.nms(bboxes, 0.45, method='nms')image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()