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YOLO_small_tf.py
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YOLO_small_tf.py
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import numpy as np
import tensorflow as tf
import cv2
import time
import sys
class YOLO_TF:
fromfile = None
tofile_img = 'test/output.jpg'
tofile_txt = 'test/output.txt'
imshow = True
filewrite_img = False
filewrite_txt = False
disp_console = True
weights_file = 'weights/YOLO_small.ckpt'
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
def __init__(self,argvs = []):
self.argv_parser(argvs)
self.build_networks()
if self.fromfile is not None: self.detect_from_file(self.fromfile)
def argv_parser(self,argvs):
for i in range(1,len(argvs),2):
if argvs[i] == '-fromfile' : self.fromfile = argvs[i+1]
if argvs[i] == '-tofile_img' : self.tofile_img = argvs[i+1] ; self.filewrite_img = True
if argvs[i] == '-tofile_txt' : self.tofile_txt = argvs[i+1] ; self.filewrite_txt = True
if argvs[i] == '-imshow' :
if argvs[i+1] == '1' :self.imshow = True
else : self.imshow = False
if argvs[i] == '-disp_console' :
if argvs[i+1] == '1' :self.disp_console = True
else : self.disp_console = False
def build_networks(self):
if self.disp_console : print "Building YOLO_small graph..."
self.x = tf.placeholder('float32',[None,448,448,3])
self.conv_1 = self.conv_layer(1,self.x,64,7,2)
self.pool_2 = self.pooling_layer(2,self.conv_1,2,2)
self.conv_3 = self.conv_layer(3,self.pool_2,192,3,1)
self.pool_4 = self.pooling_layer(4,self.conv_3,2,2)
self.conv_5 = self.conv_layer(5,self.pool_4,128,1,1)
self.conv_6 = self.conv_layer(6,self.conv_5,256,3,1)
self.conv_7 = self.conv_layer(7,self.conv_6,256,1,1)
self.conv_8 = self.conv_layer(8,self.conv_7,512,3,1)
self.pool_9 = self.pooling_layer(9,self.conv_8,2,2)
self.conv_10 = self.conv_layer(10,self.pool_9,256,1,1)
self.conv_11 = self.conv_layer(11,self.conv_10,512,3,1)
self.conv_12 = self.conv_layer(12,self.conv_11,256,1,1)
self.conv_13 = self.conv_layer(13,self.conv_12,512,3,1)
self.conv_14 = self.conv_layer(14,self.conv_13,256,1,1)
self.conv_15 = self.conv_layer(15,self.conv_14,512,3,1)
self.conv_16 = self.conv_layer(16,self.conv_15,256,1,1)
self.conv_17 = self.conv_layer(17,self.conv_16,512,3,1)
self.conv_18 = self.conv_layer(18,self.conv_17,512,1,1)
self.conv_19 = self.conv_layer(19,self.conv_18,1024,3,1)
self.pool_20 = self.pooling_layer(20,self.conv_19,2,2)
self.conv_21 = self.conv_layer(21,self.pool_20,512,1,1)
self.conv_22 = self.conv_layer(22,self.conv_21,1024,3,1)
self.conv_23 = self.conv_layer(23,self.conv_22,512,1,1)
self.conv_24 = self.conv_layer(24,self.conv_23,1024,3,1)
self.conv_25 = self.conv_layer(25,self.conv_24,1024,3,1)
self.conv_26 = self.conv_layer(26,self.conv_25,1024,3,2)
self.conv_27 = self.conv_layer(27,self.conv_26,1024,3,1)
self.conv_28 = self.conv_layer(28,self.conv_27,1024,3,1)
self.fc_29 = self.fc_layer(29,self.conv_28,512,flat=True,linear=False)
self.fc_30 = self.fc_layer(30,self.fc_29,4096,flat=False,linear=False)
#skip dropout_31
self.fc_32 = self.fc_layer(32,self.fc_30,1470,flat=False,linear=True)
self.sess = tf.Session()
self.sess.run(tf.initialize_all_variables())
self.saver = tf.train.Saver()
self.saver.restore(self.sess,self.weights_file)
if self.disp_console : print "Loading complete!" + '\n'
def conv_layer(self,idx,inputs,filters,size,stride):
channels = inputs.get_shape()[3]
weight = tf.Variable(tf.truncated_normal([size,size,int(channels),filters], stddev=0.1))
biases = tf.Variable(tf.constant(0.1, shape=[filters]))
pad_size = size//2
pad_mat = np.array([[0,0],[pad_size,pad_size],[pad_size,pad_size],[0,0]])
inputs_pad = tf.pad(inputs,pad_mat)
conv = tf.nn.conv2d(inputs_pad, weight, strides=[1, stride, stride, 1], padding='VALID',name=str(idx)+'_conv')
conv_biased = tf.add(conv,biases,name=str(idx)+'_conv_biased')
if self.disp_console : print ' Layer %d : Type = Conv, Size = %d * %d, Stride = %d, Filters = %d, Input channels = %d' % (idx,size,size,stride,filters,int(channels))
return tf.maximum(self.alpha*conv_biased,conv_biased,name=str(idx)+'_leaky_relu')
def pooling_layer(self,idx,inputs,size,stride):
if self.disp_console : print ' Layer %d : Type = Pool, Size = %d * %d, Stride = %d' % (idx,size,size,stride)
return tf.nn.max_pool(inputs, ksize=[1, size, size, 1],strides=[1, stride, stride, 1], padding='SAME',name=str(idx)+'_pool')
def fc_layer(self,idx,inputs,hiddens,flat = False,linear = False):
input_shape = inputs.get_shape().as_list()
if flat:
dim = input_shape[1]*input_shape[2]*input_shape[3]
inputs_transposed = tf.transpose(inputs,(0,3,1,2))
inputs_processed = tf.reshape(inputs_transposed, [-1,dim])
else:
dim = input_shape[1]
inputs_processed = inputs
weight = tf.Variable(tf.truncated_normal([dim,hiddens], stddev=0.1))
biases = tf.Variable(tf.constant(0.1, shape=[hiddens]))
if self.disp_console : print ' Layer %d : Type = Full, Hidden = %d, Input dimension = %d, Flat = %d, Activation = %d' % (idx,hiddens,int(dim),int(flat),1-int(linear))
if linear : return tf.add(tf.matmul(inputs_processed,weight),biases,name=str(idx)+'_fc')
ip = tf.add(tf.matmul(inputs_processed,weight),biases)
return tf.maximum(self.alpha*ip,ip,name=str(idx)+'_fc')
def detect_from_cvmat(self,img):
s = time.time()
self.h_img,self.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
in_dict = {self.x: inputs}
net_output = self.sess.run(self.fc_32,feed_dict=in_dict)
self.result = self.interpret_output(net_output[0])
self.show_results(img,self.result)
strtime = str(time.time()-s)
if self.disp_console : print 'Elapsed time : ' + strtime + ' secs' + '\n'
def detect_from_file(self,filename):
if self.disp_console : print 'Detect from ' + filename
img = cv2.imread(filename)
#img = misc.imread(filename)
self.detect_from_cvmat(img)
def detect_from_crop_sample(self):
self.w_img = 640
self.h_img = 420
f = np.array(open('person_crop.txt','r').readlines(),dtype='float32')
inputs = np.zeros((1,448,448,3),dtype='float32')
for c in range(3):
for y in range(448):
for x in range(448):
inputs[0,y,x,c] = f[c*448*448+y*448+x]
in_dict = {self.x: inputs}
net_output = self.sess.run(self.fc_32,feed_dict=in_dict)
self.boxes, self.probs = self.interpret_output(net_output[0])
img = cv2.imread('person.jpg')
self.show_results(self.boxes,img)
def interpret_output(self,output):
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] *= self.w_img
boxes[:,:,:,1] *= self.h_img
boxes[:,:,:,2] *= self.w_img
boxes[:,:,:,3] *= self.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>=self.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 self.iou(boxes_filtered[i],boxes_filtered[j]) > self.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([self.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 result
def show_results(self,img,results):
img_cp = img.copy()
if self.filewrite_txt :
ftxt = open(self.tofile_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
if self.disp_console : print ' class : ' + results[i][0] + ' , [x,y,w,h]=[' + str(x) + ',' + str(y) + ',' + str(int(results[i][3])) + ',' + str(int(results[i][4]))+'], Confidence = ' + str(results[i][5])
if self.filewrite_img or self.imshow:
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)
if self.filewrite_txt :
ftxt.write(results[i][0] + ',' + str(x) + ',' + str(y) + ',' + str(w) + ',' + str(h)+',' + str(results[i][5]) + '\n')
if self.filewrite_img :
if self.disp_console : print ' image file writed : ' + self.tofile_img
cv2.imwrite(self.tofile_img,img_cp)
if self.imshow :
cv2.imshow('YOLO_small detection',img_cp)
cv2.waitKey(1)
if self.filewrite_txt :
if self.disp_console : print ' txt file writed : ' + self.tofile_txt
ftxt.close()
def iou(self,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 training(self): #TODO add training function!
return None
def main(argvs):
yolo = YOLO_TF(argvs)
cv2.waitKey(1000)
if __name__=='__main__':
main(sys.argv)