YOLO_tensorflow-master運行與參考記錄 模型儲存與運行

YOLO_tensorflow-master代碼下載：

https://github.com/gliese581gg/YOLO_tensorflow

1.模型介紹
YOLO_tensorflow

(Version 0.2, Last updated :2016.02.16) 1.Introduction

This is tensorflow implementation of the YOLO:Real-Time Object Detection

It can only do predictions using pretrained YOLO_small & YOLO_tiny network for now.

I'm gonna support training later.

I extracted weight values from darknet's (.weight) files.

Original code(C implementation) & paper : http://pjreddie.com/darknet/yolo/ 2.Install

(1) Download code

(2) Download YOLO weight file from

YOLO_small : https://drive.google.com/file/d/0B2JbaJSrWLpza08yS2FSUnV2dlE/view?usp=sharing

YOLO_tiny : https://drive.google.com/file/d/0B2JbaJSrWLpza0FtQlc3ejhMTTA/view?usp=sharing

(3) Put the 'YOLO_(version).ckpt' in the 'weight' folder of downloaded code 3.Usage

(1) direct usage with default settings (display on console, show output image, no output file writing)

python YOLO_(small or tiny)_tf.py -fromfile (input image filename)

(2) direct usage with custom settings

python YOLO_(small or tiny)_tf.py argvswhere argvs are-fromfile (input image filename) : input image file-disp_console (0 or 1) : whether display results on terminal or not-imshow (0 or 1) : whether display result image or not-tofile_img (output image filename) : output image file-tofile_txt (output txt filename) : output text file (contains class, x, y, w, h, probability)

(3) import on other scripts

import YOLO_(small or tiny)_tfyolo = YOLO_(small or tiny)_tf.YOLO_TF()yolo.disp_console = (True or False, default = True)yolo.imshow = (True or False, default = True)yolo.tofile_img = (output image filename)yolo.tofile_txt = (output txt filename)yolo.filewrite_img = (True or False, default = False)yolo.filewrite_txt = (True of False, default = False)yolo.detect_from_file(filename)yolo.detect_from_cvmat(cvmat)

4.Requirements Tensorflow Opencv2 5.Copyright

According to the LICENSE file of the original code, Me and original author hold no liability for any damages Do not use this on commercial! 6.Changelog

2016/02/15 : First upload!

2016/02/16 : Added YOLO_tiny, Fixed bug that ignores one of the boxes in grid when both boxes detected valid objects

2016/08/26 : Uploaded weight file converter! (darknet weight -> tensorflow ckpt)

2.模型使用

我使用YOLO時

出現

cv2.imread('./test/person.jpg')

讀取到的圖片為None.

解決辦法 在代碼最前面加

import cv2

如果把 import cv2添加到 import YOLO_tiny_tf後面一樣報None.擷取不到圖片

下面是我調研yolo的所有代碼。

#encoding:utf-8import cv2import YOLO_tiny_tfyolo = YOLO_tiny_tf.YOLO_TF()yolo.disp_console = Trueyolo.imshow = Trueyolo.tofile_img = './test/ttt.jpg'yolo.tofile_txt = './test/ttt.txt'yolo.filewrite_img = Trueyolo.filewrite_txt = Truefilename = './test/person.jpg'# 讀入映像#im = cv2.imread('./test/person.jpg')yolo.detect_from_file(filename)#yolo.detect_from_cvmat(im)運行結果

3.模型儲存與運行

（1）.將YOLO_ting_tf.py中的輸入添加名字input，代碼如下：

def build_networks(self):   if self.disp_console : print "Building YOLO_tiny graph..."   self.x = tf.placeholder('float32',[None,448,448,3],name="input")

（2）.運行save_graph模型和權重一起儲存

import osimport cv2import tensorflow as tfimport numpy as npfrom tensorflow.python.framework import test_utilimport freeze_graphfrom YOLO_tiny_tf import YOLO_TFdef save_graph(sess,output_path,checkpoint,checkpoint_state_name,input_graph_name,output_graph_name):    checkpoint_prefix = os.path.join(output_path,checkpoint)    saver = tf.train.Saver(tf.all_variables())    saver.save(sess, checkpoint_prefix, global_step=0,latest_filename=checkpoint_state_name)    tf.train.write_graph(sess.graph.as_graph_def(),output_path,                           input_graph_name)    # We save out the graph to disk, and then call the const conversion    # routine.    input_graph_path = os.path.join(output_path, input_graph_name)    input_saver_def_path = ""    input_binary = False    input_checkpoint_path = checkpoint_prefix + "-0"    output_node_names = "19_fc"    restore_op_name = "save/restore_all"    filename_tensor_name = "save/Const:0"    output_graph_path = os.path.join(output_path, output_graph_name)    clear_devices = False    freeze_graph.freeze_graph(input_graph_path, input_saver_def_path,                              input_binary, input_checkpoint_path,                              output_node_names, restore_op_name,                              filename_tensor_name, output_graph_path,clear_devices, "")yolo=YOLO_TF()#with open("weights/small_model.pb","wb") as f: #   f.write(yolo.sess.graph.as_graph_def().SerializeToString())save_graph(yolo.sess,"/home/acer/pbMake/yolo","saved_checkpoint","checkpoint_state","yoloting_input_graph.pb","yoloting_output_graph.pb")

（3）.讀取剛才保持的 yoloting_output_graph.pb,進行測試和檢測

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport cv2import tensorflow as tfimport numpy as npdef iou(box1, box2):    tb = min(box1[0] + 0.5 * box1[2], box2[0] + 0.5 * box2[2]) - max(box1[0] - 0.5 * box1[2], box2[0] - 0.5 * box2[2])    lr = min(box1[1] + 0.5 * box1[3], box2[1] + 0.5 * box2[3]) - max(box1[1] - 0.5 * box1[3], box2[1] - 0.5 * box2[3])    if tb < 0 or lr < 0:        intersection = 0    else:        intersection = tb * lr    return intersection / (box1[2] * box1[3] + box2[2] * box2[3] - intersection)def interpret_output(output):    alpha = 0.1    threshold = 0.2    iou_threshold = 0.5    num_class = 20    num_box = 2    grid_size = 7    classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",               "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]    w_img = 640    h_img = 480    probs = np.zeros((7, 7, 2, 20))    class_probs = np.reshape(output[0:980], (7, 7, 20))    scales = np.reshape(output[980:1078], (7, 7, 2))    boxes = np.reshape(output[1078:], (7, 7, 2, 4))    offset = np.transpose(np.reshape(np.array([np.arange(7)] * 14), (2, 7, 7)), (1, 2, 0))    boxes[:, :, :, 0] += offset    boxes[:, :, :, 1] += np.transpose(offset, (1, 0, 2))    boxes[:, :, :, 0:2] = boxes[:, :, :, 0:2] / 7.0    boxes[:, :, :, 2] = np.multiply(boxes[:, :, :, 2], boxes[:, :, :, 2])    boxes[:, :, :, 3] = np.multiply(boxes[:, :, :, 3], boxes[:, :, :, 3])    boxes[:, :, :, 0] *= w_img    boxes[:, :, :, 1] *= h_img    boxes[:, :, :, 2] *= w_img    boxes[:, :, :, 3] *= h_img    for i in range(2):        for j in range(20):            probs[:, :, i, j] = np.multiply(class_probs[:, :, j], scales[:, :, i])    filter_mat_probs = np.array(probs >= threshold, dtype='bool')    filter_mat_boxes = np.nonzero(filter_mat_probs)    boxes_filtered = boxes[filter_mat_boxes[0], filter_mat_boxes[1], filter_mat_boxes[2]]    probs_filtered = probs[filter_mat_probs]    classes_num_filtered = np.argmax(filter_mat_probs, axis=3)[        filter_mat_boxes[0], filter_mat_boxes[1], filter_mat_boxes[2]]    argsort = np.array(np.argsort(probs_filtered))[::-1]    boxes_filtered = boxes_filtered[argsort]    probs_filtered = probs_filtered[argsort]    classes_num_filtered = classes_num_filtered[argsort]    for i in range(len(boxes_filtered)):        if probs_filtered[i] == 0: continue        for j in range(i + 1, len(boxes_filtered)):            if iou(boxes_filtered[i], boxes_filtered[j]) > iou_threshold:                probs_filtered[j] = 0.0    filter_iou = np.array(probs_filtered > 0.0, dtype='bool')    boxes_filtered = boxes_filtered[filter_iou]    probs_filtered = probs_filtered[filter_iou]    classes_num_filtered = classes_num_filtered[filter_iou]    result = []    for i in range(len(boxes_filtered)):        result.append(            [classes[classes_num_filtered[i]], boxes_filtered[i][0], boxes_filtered[i][1], boxes_filtered[i][2],             boxes_filtered[i][3], probs_filtered[i]])    return resultdef show_results(img, results):    filewrite_img = False    filewrite_txt = True    img_cp = img.copy()    if filewrite_txt:        ftxt = open('./test/xsss.txt', 'w')    for i in range(len(results)):        x = int(results[i][1])        y = int(results[i][2])        w = int(results[i][3]) // 2        h = int(results[i][4]) // 2        cv2.rectangle(img_cp, (x - w, y - h), (x + w, y + h), (0, 255, 0), 2)        cv2.rectangle(img_cp, (x - w, y - h - 20), (x + w, y - h), (125, 125, 125), -1)        cv2.putText(img_cp, results[i][0] + ' : %.2f' % results[i][5], (x - w + 5, y - h - 7),        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)        ftxt.write(results[i][0] + ',' + str(x) + ',' + str(y) + ',' + str(w) + ',' + str(h) + ',' + str(                results[i][5]) + '\n')        cv2.imwrite('./test/xlsld.jpg', img_cp)# produces the expected result.with tf.Graph().as_default():    output_graph_def = tf.GraphDef()    output_graph_path = '/home/acer/pbMake/yolo/yoloting_output_graph.pb'    x = tf.placeholder('float32', [None, 448, 448, 3])    with open(output_graph_path, "rb") as f:        output_graph_def.ParseFromString(f.read())        _ = tf.import_graph_def(output_graph_def, name="")    with tf.Session() as sess:        tf.initialize_all_variables().run()        input_x = sess.graph.get_tensor_by_name("input:0")        print(input_x)        output = sess.graph.get_tensor_by_name("19_fc:0")        print(output)        filename = './test/person.jpg'        img = cv2.imread(filename)        h_img, w_img, _ = img.shape        img_resized = cv2.resize(img, (448, 448))        img_RGB = cv2.cvtColor(img_resized, cv2.COLOR_BGR2RGB)        img_resized_np = np.asarray(img_RGB)        inputs = np.zeros((1, 448, 448, 3), dtype='float32')        inputs[0] = (img_resized_np / 255.0) * 2.0 - 1.0        #input_node = sess.graph.get_operation_by_name("input")        in_dict = {input_x: inputs}        net_output = sess.run(output, {input_x: inputs})        print("net_output", net_output)        #net_output = sess.run(output_node, feed_dict=in_dict)        result = interpret_output(net_output[0])        show_results(img, result)

運行結果，與直接運行模型的結果一樣。