forked from TensorLayer/tensorlayer3
305 lines
14 KiB
Plaintext
305 lines
14 KiB
Plaintext
import colorsys
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import copy
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import os
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import time
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import numpy as np
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import tensorflow as tf
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from PIL import Image, ImageDraw, ImageFont
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# from tensorflow.keras.applications.imagenet_utils import preprocess_input
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import nets.frcnn as frcnn
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from nets.frcnn_training import get_new_img_size
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from utils.anchors import get_anchors
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from utils.config import Config
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from utils.utils import BBoxUtility
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#--------------------------------------------#
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# 使用自己训练好的模型预测需要修改2个参数
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# model_path和classes_path都需要修改！
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# 如果出现shape不匹配
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# 一定要注意训练时的NUM_CLASSES、
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# model_path和classes_path参数的修改
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#--------------------------------------------#
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class FRCNN(object):
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_defaults = {
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"model_path" : 'model_data/voc_weights.h5',
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"classes_path" : 'model_data/voc_classes.txt',
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"confidence" : 0.5,
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"iou" : 0.3
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}
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@classmethod
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def get_defaults(cls, n):
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if n in cls._defaults:
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return cls._defaults[n]
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else:
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return "Unrecognized attribute name '" + n + "'"
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#---------------------------------------------------#
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# 初始化faster RCNN
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#---------------------------------------------------#
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def __init__(self, **kwargs):
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self.__dict__.update(self._defaults)
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self.class_names = self._get_class()
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self.config = Config()
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self.generate()
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self.bbox_util = BBoxUtility()
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#---------------------------------------------------#
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# 获得所有的分类
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#---------------------------------------------------#
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def _get_class(self):
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classes_path = os.path.expanduser(self.classes_path)
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with open(classes_path) as f:
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class_names = f.readlines()
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class_names = [c.strip() for c in class_names]
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return class_names
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#---------------------------------------------------#
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# 获得所有的分类
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#---------------------------------------------------#
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def generate(self):
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model_path = os.path.expanduser(self.model_path)
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assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
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#-------------------------------#
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# 计算总的类的数量
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#-------------------------------#
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self.num_classes = len(self.class_names)+1
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#-------------------------------#
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# 载入模型与权值
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#-------------------------------#
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self.model_rpn, self.model_classifier = frcnn.get_predict_model(self.config, self.num_classes)
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self.model_rpn.load_weights(self.model_path, by_name=True)
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self.model_classifier.load_weights(self.model_path, by_name=True)
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print('{} model, anchors, and classes loaded.'.format(model_path))
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# 画框设置不同的颜色
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hsv_tuples = [(x / len(self.class_names), 1., 1.)
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for x in range(len(self.class_names))]
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self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
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self.colors = list(
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map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
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self.colors))
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#---------------------------------------------------#
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# 用于计算共享特征层的大小
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#---------------------------------------------------#
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def get_img_output_length(self, width, height):
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def get_output_length(input_length):
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# input_length += 6
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filter_sizes = [7, 3, 1, 1]
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padding = [3,1,0,0]
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stride = 2
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for i in range(4):
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# input_length = (input_length - filter_size + stride) // stride
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input_length = (input_length+2*padding[i]-filter_sizes[i]) // stride + 1
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return input_length
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return get_output_length(width), get_output_length(height)
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@tf.function(experimental_relax_shapes=True)
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def model_rpn_get_pred(self, photo):
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preds = self.model_rpn(photo, training=False)
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return preds
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@tf.function(experimental_relax_shapes=True)
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def model_classifier_get_pred(self, photo):
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preds = self.model_classifier(photo, training=False)
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return preds
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#---------------------------------------------------#
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# 检测图片
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#---------------------------------------------------#
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def detect_image(self, image):
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#-------------------------------------#
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# 转换成RGB图片，可以用于灰度图预测。
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#-------------------------------------#
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image = image.convert("RGB")
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image_shape = np.array(np.shape(image)[0:2])
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old_width, old_height = image_shape[1], image_shape[0]
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old_image = copy.deepcopy(image)
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#---------------------------------------------------------#
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# 给原图像进行resize，resize到短边为600的大小上
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#---------------------------------------------------------#
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width, height = get_new_img_size(old_width, old_height)
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image = image.resize([width,height], Image.BICUBIC)
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photo = np.array(image,dtype = np.float32)
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#-----------------------------------------------------------#
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# 图片预处理，归一化。
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#-----------------------------------------------------------#
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# photo = preprocess_input(np.expand_dims(photo,0))
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photo = np.expand_dims(photo, 0)/255.
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rpn_pred = self.model_rpn_get_pred(photo)
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rpn_pred = [x.numpy() for x in rpn_pred]
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#-----------------------------------------------------------#
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# 将建议框网络的预测结果进行解码
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#-----------------------------------------------------------#
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base_feature_width, base_feature_height = self.get_img_output_length(width, height)
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anchors = get_anchors([base_feature_width, base_feature_height], width, height)
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rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
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#-------------------------------------------------------------#
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# 在获得建议框和共享特征层后，将二者传入classifier中进行预测
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#-------------------------------------------------------------#
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base_layer = rpn_pred[2]
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proposal_box = np.array(rpn_results)[:, :, 1:]
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temp_ROIs = np.zeros_like(proposal_box)
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temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
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classifier_pred = self.model_classifier_get_pred([base_layer, temp_ROIs])
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classifier_pred = [x.numpy() for x in classifier_pred]
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#-------------------------------------------------------------#
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# 利用classifier的预测结果对建议框进行解码，获得预测框
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#-------------------------------------------------------------#
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results = self.bbox_util.detection_out_classifier(classifier_pred, proposal_box, self.config, self.confidence)
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if len(results[0])==0:
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return old_image
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results = np.array(results[0])
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boxes = results[:, :4]
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top_conf = results[:, 4]
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top_label_indices = results[:, 5]
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boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
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boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
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font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
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thickness = max((np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width * 2, 1)
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image = old_image
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for i, c in enumerate(top_label_indices):
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predicted_class = self.class_names[int(c)]
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score = top_conf[i]
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left, top, right, bottom = boxes[i]
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top = top - 5
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left = left - 5
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bottom = bottom + 5
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right = right + 5
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top = max(0, np.floor(top + 0.5).astype('int32'))
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left = max(0, np.floor(left + 0.5).astype('int32'))
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bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
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right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
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# 画框框
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label = '{} {:.2f}'.format(predicted_class, score)
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draw = ImageDraw.Draw(image)
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label_size = draw.textsize(label, font)
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label = label.encode('utf-8')
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print(label, top, left, bottom, right)
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if top - label_size[1] >= 0:
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text_origin = np.array([left, top - label_size[1]])
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else:
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text_origin = np.array([left, top + 1])
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for i in range(thickness):
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draw.rectangle(
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[left + i, top + i, right - i, bottom - i],
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outline=self.colors[int(c)])
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draw.rectangle(
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[tuple(text_origin), tuple(text_origin + label_size)],
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fill=self.colors[int(c)])
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draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
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del draw
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return image
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def get_FPS(self, image, test_interval):
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#-------------------------------------#
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# 转换成RGB图片，可以用于灰度图预测。
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#-------------------------------------#
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image = image.convert("RGB")
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image_shape = np.array(np.shape(image)[0:2])
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old_width, old_height = image_shape[1], image_shape[0]
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#---------------------------------------------------------#
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# 给原图像进行resize，resize到短边为600的大小上
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#---------------------------------------------------------#
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width, height = get_new_img_size(old_width, old_height)
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image = image.resize([width,height], Image.BICUBIC)
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photo = np.array(image,dtype = np.float64)
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#-----------------------------------------------------------#
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# 图片预处理，归一化。
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#-----------------------------------------------------------#
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# photo = preprocess_input(np.expand_dims(photo,0))
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photo = np.expand_dims(photo, 0)/255.
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rpn_pred = self.model_rpn.predict(photo)
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#-----------------------------------------------------------#
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# 将建议框网络的预测结果进行解码
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#-----------------------------------------------------------#
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base_feature_width, base_feature_height = self.get_img_output_length(width, height)
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anchors = get_anchors([base_feature_width, base_feature_height], width, height)
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rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
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#-------------------------------------------------------------#
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# 在获得建议框和共享特征层后，将二者传入classifier中进行预测
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#-------------------------------------------------------------#
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base_layer = rpn_pred[2]
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proposal_box = np.array(rpn_results)[:, :, 1:]
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temp_ROIs = np.zeros_like(proposal_box)
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temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
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classifier_pred = self.model_classifier.predict([base_layer, temp_ROIs])
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#-------------------------------------------------------------#
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# 利用classifier的预测结果对建议框进行解码，获得预测框
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#-------------------------------------------------------------#
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results = self.bbox_util.detection_out_classifier(classifier_pred, proposal_box, self.config, self.confidence)
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if len(results[0])>0:
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results = np.array(results[0])
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boxes = results[:, :4]
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top_conf = results[:, 4]
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top_label_indices = results[:, 5]
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boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
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boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
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t1 = time.time()
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for _ in range(test_interval):
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rpn_pred = self.model_rpn.predict(photo)
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#-----------------------------------------------------------#
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# 将建议框网络的预测结果进行解码
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#-----------------------------------------------------------#
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base_feature_width, base_feature_height = self.get_img_output_length(width, height)
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anchors = get_anchors([base_feature_width, base_feature_height], width, height)
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rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
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#-------------------------------------------------------------#
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# 在获得建议框和共享特征层后，将二者传入classifier中进行预测
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#-------------------------------------------------------------#
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base_layer = rpn_pred[2]
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proposal_box = np.array(rpn_results)[:, :, 1:]
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temp_ROIs = np.zeros_like(proposal_box)
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temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
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classifier_pred = self.model_classifier.predict([base_layer, temp_ROIs])
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#-------------------------------------------------------------#
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# 利用classifier的预测结果对建议框进行解码，获得预测框
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#-------------------------------------------------------------#
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results = self.bbox_util.detection_out_classifier(classifier_pred, proposal_box, self.config, self.confidence)
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if len(results[0])>0:
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results = np.array(results[0])
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boxes = results[:, :4]
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top_conf = results[:, 4]
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top_label_indices = results[:, 5]
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boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
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boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
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t2 = time.time()
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tact_time = (t2 - t1) / test_interval
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return tact_time
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