def run(self, image_name, get_label=False, do_detection=1):
"""detection and extraction with max score box"""
### for web demo
#caffe.set_mode_gpu()
#print "do_detection: ",do_detection
if do_detection:
t1 = Timer()
t1.tic()
image = self.detect(image_name)
t1.toc('Detect time: ')
#print "Detection has done"
else:
image = cv2.imread(image_name)
#image = imresize(im, 300)
t2 = Timer()
t2.tic()
image = pad(image,size=224)
#image = pad(image)
features = extraction.forward(self.net_e, image, self.transformer)
r = np.squeeze(features['pool5/7x7_s1'].data[0])
#features2 = extraction.forward(self.net_e2, image, self.transformer2)
#r2 = np.squeeze(features2['pool5/7x7_s1'].data[0])
#r = r2
#r = np.hstack((r, r2)).copy()
t2.toc('extract time: ')
#start = time.time()
if self.pca is not None:
r = self.pca.transform(r)[0,:]
#print 'pca time: ', time.time() - start
r = r/norm(r)
if get_label:
label = np.squeeze(features['prob'].data[0].copy())
return r, label
return r
def detect_video(detector, device, args):
"""Demo for detecting video."""
timer = Timer()
input_video = cv.VideoCapture(args.video)
frame_width = int(input_video.get(cv.CAP_PROP_FRAME_WIDTH))
frame_height = int(input_video.get(cv.CAP_PROP_FRAME_HEIGHT))
output_video = cv.VideoWriter()
if args.save:
output_video.open('record.avi', cv.VideoWriter_fourcc(*'XVID'),
input_video.get(cv.CAP_PROP_FPS),
(frame_width, frame_height), True)
frame = np.empty([frame_height, frame_width, 3], dtype=np.uint8)
while input_video.read(frame)[0]:
timer.tic()
frame = undistort(frame)
pred_points = detect_marking_points(
detector, frame, args.thresh, device)
slots = None
if pred_points and args.inference_slot:
marking_points = list(list(zip(*pred_points))[1])
slots = inference_slots(marking_points)
timer.toc()
plot_points(frame, pred_points)
plot_slots(frame, pred_points, slots)
cv.imshow('demo', frame)
cv.waitKey(1)
if args.save:
output_video.write(frame)
print("Average time: ", timer.calc_average_time(), "s.")
input_video.release()
output_video.release()
def search(self, image_path, do_detection=1, k=10):
#queryImage = cv2.imread(image_path)
t1 = Timer()
t1.tic()
#queryFeatures = descriptor.get_descriptor(image_path, multi_box=False)
queryFeatures = descriptor.get_descriptor(image_path)
t1.toc('Feature Extraction time: ')
t2 = Timer()
t2.tic()
#p = Profile()
#results = p.runcall(self.searcher.search, queryFeatures)
#p.print_stats()
results, dists, ind = self.searcher.search(queryFeatures,k=5*k)
#self.reranking(queryFeatures, results, dists, ind, 0.6)
#self.queryExpansion2(results, dists, ind)
#self.queryExpansion(queryFeatures, results, dists, ind, top=3)
t2.toc('Knn search time: ')
result = []
# origine image
#result.append(image_path)
dist = []
for j,imageName in enumerate(results):
if imageName not in result:
result.append(imageName)
dist.append(dists[j])
#print result[:k]
return result[:k],dist[:k]
def search(self, image_path, do_detection=1, k=50):
#queryImage = cv2.imread(image_path)
t1 = Timer()
t1.tic()
#queryFeatures = descriptor.get_descriptor(image_path, multi_box=False)
queryFeatures, label = descriptor.get_descriptor(image_path,
multi_box=False,
get_label=True,
do_detection=do_detection)
flag = []
#flag = [] # if do, we donot use class to filter result
t1.toc('Feature Extraction time: ')
t2 = Timer()
t2.tic()
#p = Profile()
#results = p.runcall(self.searcher.search, queryFeatures)
#p.print_stats()
results, dists = self.searcher.search(queryFeatures)
print dists
t2.toc('Knn search time: ')
result = []
# origine image
#result.append(image_path)
if len(flag) != 0:
for j in xrange(0, k):
imageName = results[j]
if imageName not in result:
#Juge class error but image similarity is high
if dists[j] < 0.05:
result.append(imageName)
continue
#if dists[j] > 0.2:
# break
#judge wether image belongs to the class
image_path = imageName.split('/')
image_dir = image_path[0]+'/'+image_path[1]+'/'+image_path[2]
#print image_dir
if image_dir in flag:
result.append(imageName)
#else:
# result.append(imageName)
print 'total result', len(result)
if len(result)<3:
# if result about class is less than 5, we do search in all datasets
#print 'total result', len(result)
k = 30
result = []
for j in xrange(0, k):
imageName = results[j]
if imageName not in result:
#if dists[j] > 0.2:
# break
result.append(imageName)
return result
def detect_image(detector, device, args):
"""Demo for detecting images."""
timer = Timer()
while True:
image_file = input('Enter image file path: ')
image = cv.imread(image_file)
timer.tic()
pred_points = detect_marking_points(detector, image, args.thresh,
device)
slots = None
if pred_points and args.inference_slot:
marking_points = list(list(zip(*pred_points))[1])
slots = inference_slots(marking_points)
timer.toc()
plot_points(image, pred_points)
plot_slots(image, pred_points, slots)
cv.imshow('demo', image)
cv.waitKey(1)
if args.save:
cv.imwrite('save.jpg', image, [int(cv.IMWRITE_JPEG_QUALITY), 100])
def search(self, image_path, do_detection=0, k=20):
t1 = Timer()
t1.tic()
#queryFeatures = descriptor.get_descriptor(image_path, multi_box=False)
queryFeatures = descriptor.get_descriptor(image_path,do_detection=do_detection)
t1.toc('Feature Extraction time: ')
t2 = Timer()
t2.tic()
results, dists, ind = self.searcher.search(queryFeatures,k=k)
#self.queryExpansion(results, dists, ind)
#self.queryExpansion(results, dists, ind)
t2.toc('Knn search time: ')
result = []
dist = []
for j,imageName in enumerate(results):
if imageName not in result:
result.append(imageName)
dist.append(dists[j])
return result[:k],dist[:k]
def detect(self, image_name, multi_box=False, classes=CLASSES[1:]):
"""detect clothes in image"""
t1 = Timer()
t1.tic()
im = cv2.imread(image_name)
#im = imresize(im)
t1.toc('read image time: ')
t2 = Timer()
t2.tic()
scores, boxes = im_detect(self.net_d, im)
t2.toc('faster-rcnn time: ')
CONF_THRESH = 0.8
NMS_THRESH = 0.3
#dets = np.ones((5))
max_score = 0
if not multi_box:
f = open('bbox.txt','w')
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
box = np.hstack((cls_boxes, cls_scores[:,
np.newaxis])).astype(np.float32)
keep = nms(box, NMS_THRESH)
cls_boxes = box[keep, :4]
cls_scores = box[keep, 4]
score = max(cls_scores)
if score > CONF_THRESH:
ind = cls_scores.argmax()
max_score = score
det = cls_boxes[ind,:]
f.write(cls)
f.write('\n')
for d in det:
f.write(str(d))
f.write(' ')
#print cls
break
f.close()
if max_score == 0:
return im
else:
bbox = det.astype(np.float32)
#imshow(crop(im,bbox))
return crop(im,bbox)
else:
multi_im = []
dets = []
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
box = np.hstack((cls_boxes, cls_scores[:,
np.newaxis])).astype(np.float32)
keep = nms(box, NMS_THRESH)
cls_boxes = box[keep, :4]
cls_scores = box[keep, 4]
score = max(cls_scores)
if score > CONF_THRESH:
max_score = score
ind = cls_scores.argmax()
det = cls_boxes[ind,:]
if cls == '7' and (det[3]-det[1])<im.shape[0]/4 and det[3]>im.shape[0]-30:
break
if cls == '1' or cls == '4':
dets.append(det.copy())
break
dets.append(det.copy())
if max_score == 0:
multi_im.append(im)
return multi_im
else:
for det in dets:
bbox = det.astype(np.float32)
multi_im.append(crop(im,bbox))
return multi_im
