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yolo_main.py
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yolo_main.py
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import caffe
GPU_ID = 0 # Switch between 0 and 1 depending on the GPU you want to use.
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
# caffe.set_mode_cpu()
from datetime import datetime
import numpy as np
import sys, getopt
import cv2
def interpret_output(output, img_width, img_height):
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train","tvmonitor"]
w_img = img_width
h_img = img_height
print w_img, h_img
threshold = 0.2
iou_threshold = 0.5
num_class = 20
num_box = 2
grid_size = 7
probs = np.zeros((7,7,2,20))
class_probs = np.reshape(output[0:980],(7,7,20))
# print class_probs
scales = np.reshape(output[980:1078],(7,7,2))
# print scales
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(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 result
def 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 show_results(img,results, img_width, img_height):
img_cp = img.copy()
disp_console = True
imshow = True
# 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 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])
xmin = x-w
xmax = x+w
ymin = y-h
ymax = y+h
if xmin<0:
xmin = 0
if ymin<0:
ymin = 0
if xmax>img_width:
xmax = img_width
if ymax>img_height:
ymax = img_height
if imshow:
cv2.rectangle(img_cp,(xmin,ymin),(xmax,ymax),(0,255,0),2)
print xmin, ymin, xmax, ymax
cv2.rectangle(img_cp,(xmin,ymin-20),(xmax,ymin),(125,125,125),-1)
cv2.putText(img_cp,results[i][0] + ' : %.2f' % results[i][5],(xmin+5,ymin-7),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
if imshow :
cv2.imshow('YOLO detection',img_cp)
cv2.waitKey(1000)
def main(argv):
model_filename = ''
weight_filename = ''
img_filename = ''
try:
opts, args = getopt.getopt(argv, "hm:w:i:")
print opts
except getopt.GetoptError:
print 'yolo_main.py -m <model_file> -w <output_file> -i <img_file>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'yolo_main.py -m <model_file> -w <weight_file> -i <img_file>'
sys.exit()
elif opt == "-m":
model_filename = arg
elif opt == "-w":
weight_filename = arg
elif opt == "-i":
img_filename = arg
print 'model file is "', model_filename
print 'weight file is "', weight_filename
print 'image file is "', img_filename
net = caffe.Net(model_filename, weight_filename, caffe.TEST)
img = caffe.io.load_image(img_filename) # load the image using caffe io
inputs = img
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
start = datetime.now()
out = net.forward_all(data=np.asarray([transformer.preprocess('data', inputs)]))
end = datetime.now()
elapsedTime = end-start
print 'total time is " milliseconds', elapsedTime.total_seconds()*1000
print out.iteritems()
img_cv = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
results = interpret_output(out['result'][0], img.shape[1], img.shape[0]) # fc27 instead of fc12 for yolo_small
show_results(img_cv,results, img.shape[1], img.shape[0])
cv2.waitKey(10000)
if __name__=='__main__':
main(sys.argv[1:])