def main(lfw_list_fn,
lfw_root,
save_dir,
save_img=False,
show_img=False):
minsize = 20
caffe_model_path = "../../model"
threshold = [0.6, 0.7, 0.7]
scale_factor = 0.709
if not osp.exists(save_dir):
os.makedirs(save_dir)
fp_rlt = open(osp.join(save_dir, 'lfw_mtcnn_fd_rlt.json'), 'w')
# result_list = []
fp_rlt.write('[\n')
t1 = time.clock()
detector = MtcnnDetector(caffe_model_path)
t2 = time.clock()
print("initFaceDetector() costs %f seconds" % (t2 - t1))
fp = open(lfw_list_fn, 'r')
ttl_time = 0.0
img_cnt = 0
for line in fp:
imgpath = line.strip()
print("\n===>" + imgpath)
if imgpath == '':
print 'empty line, not a file name, skip to next'
continue
if imgpath[0] == '#':
print 'skip line starts with #, skip to next'
continue
splits = imgpath.split()
imgpath = splits[0]
id = 'unkown' if len(splits) < 2 else splits[1]
if not imgpath.startswith('/'):
fullpath = osp.join(lfw_root, imgpath)
else:
fullpath = imgpath
rlt = {}
rlt["filename"] = imgpath
rlt["faces"] = []
rlt['face_count'] = 0
rlt['id'] = id
try:
img = cv2.imread(fullpath)
except:
print('failed to load image: ' + fullpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
if img is None:
print('failed to load image: ' + fullpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
img_cnt += 1
t1 = time.clock()
bboxes, points = detector.detect_face(img, minsize,
threshold, scale_factor)
t2 = time.clock()
ttl_time += t2 - t1
print("detect_face() costs %f seconds" % (t2 - t1))
if len(bboxes) > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4],
'score': box[4],
'pts': pts
}
rlt['faces'].append(tmp)
rlt['face_count'] = len(bboxes)
rlt['message'] = 'success'
# result_list.append(rlt)
s = json.dumps(rlt, indent=2)
fp_rlt.write(s + ',\n')
# fp_rlt.write(',\n' + s)
# print('output bboxes: ' + str(bboxes))
# print('output points: ' + str(points))
# toc()
if bboxes is None:
continue
print("\n===> Processed %d images, costs %f seconds, avg time: %f seconds" % (
img_cnt, ttl_time, ttl_time / img_cnt))
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(imgpath))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
# json.dump(result_list, fp_rlt, indent=2)
# print fp_rlt.tell()
# delete the last ','
if sys.platform is 'win32':
fp_rlt.seek(-3, 1)
else:
fp_rlt.seek(-2, 1)
fp_rlt.write('\n]')
fp_rlt.close()
fp.close()
if show_img:
cv2.destroyAllWindows()
def main(save_dir=None, save_img=False, show_img=False):
minsize = 20
caffe_model_path = MODEL_PATH
threshold = [0.6, 0.7, OUTPUT_THRESHOLD]
scale_factor = 0.709
if not save_dir:
save_dir = './fd_rlt'
if not osp.exists(save_dir):
os.makedirs(save_dir)
fp_time = open(osp.join(save_dir, 'fd_time.txt'), 'w')
t1 = time.clock()
detector = MtcnnDetector(caffe_model_path)
t2 = time.clock()
msg = "initFaceDetector() costs %f seconds" % (t2 - t1)
print(msg)
fp_time.write(msg + '\n')
ttl_time = 0.0
img_cnt = 0
for k in range(1, FOLDS_CNT + 1):
k_str = str(k)
if k != 10:
k_str = "0" + k_str
fn_list = osp.join(FDDB_FOLDS_DIR, "FDDB-fold-" + k_str + ".txt")
fn_fd_rlt = osp.join(save_dir, "fold-" + k_str + "-out.txt")
print('===========================')
print('Process image list: ' + fn_list)
print('Save results into: ' + fn_fd_rlt)
fp_list = open(fn_list, 'r')
fp_fd_rlt = open(fn_fd_rlt, 'w')
for line in fp_list:
imgname = line.strip()
imgpath = osp.join(FDDB_IMG_ROOT_DIR, imgname + ".jpg")
msg = "---> " + imgpath
print(msg)
fp_time.write(msg + 'n')
img = cv2.imread(imgpath)
if img is None:
raise Exception('failed to load image: ' + imgpath)
resize_factor = 1.0
if DO_RESIZE:
print('original image shape: {}'.format(img.shape))
ht, wd, chs = img.shape
if ht > wd:
resize_factor = float(RESIZED_LONG_SIDE) / ht
else:
resize_factor = float(RESIZED_LONG_SIDE) / wd
wd_new = int(resize_factor * wd)
ht_new = int(resize_factor * ht)
resized_img = cv2.resize(img, (wd_new, ht_new))
print('resized image shape: {}'.format(resized_img.shape))
# if show_img:
# cv2.imshow('resied_img', resized_img)
# ch = cv2.waitKey(0) & 0xFF
# if ch == 27:
# break
else:
resized_img = img
resize_factor_inv = 1.0 / resize_factor
img_cnt += 1
t1 = time.clock()
bboxes, points = detector.detect_face(resized_img, minsize,
threshold, scale_factor)
t2 = time.clock()
ttl_time += t2 - t1
msg = "detect_face() costs %f seconds" % (t2 - t1)
print(msg)
fp_time.write(msg + '\n')
fp_fd_rlt.write(imgname + "\n")
fp_fd_rlt.write(str(len(bboxes)) + "\n")
print points
if DO_RESIZE:
for i in range(len(bboxes)):
for j in range(4):
bboxes[i][j] *= resize_factor_inv
for j in range(10):
points[i][j] *= resize_factor_inv
for i in range(len(bboxes)):
fp_fd_rlt.write(str(bboxes[i][0]) + " ")
fp_fd_rlt.write(str(bboxes[i][1]) + " ")
fp_fd_rlt.write(str(bboxes[i][2] - bboxes[i][0]) + " ")
fp_fd_rlt.write(str(bboxes[i][3] - bboxes[i][1]) + " ")
fp_fd_rlt.write(str(bboxes[i][4]) + "\n")
fp_fd_rlt.flush()
msg = "===> Processed %d images, costs %f seconds, avg time: %f seconds" % (
img_cnt, ttl_time, ttl_time / img_cnt)
print(msg)
fp_time.write(msg + '\n')
fp_time.flush()
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(imgpath))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
fp_list.close()
fp_fd_rlt.close()
fp_time.close()
if show_img:
cv2.destroyAllWindows()
def main(img_list_fn, root_dir, save_dir, save_img=True, show_img=False):
MAX_LINE = 500
minsize = 20
caffe_model_path = "../../model"
threshold = [0.6, 0.7, 0.7]
scale_factor = 0.709
if not osp.exists(save_dir):
os.makedirs(save_dir)
fp = open(img_list_fn, 'r')
# if not SAVE_INTO_SEPERATE_JSON:
#fp_rlt = open(osp.join(save_dir, 'mtcnn_fd_rlt.json'), 'w')
result_list = []
t1 = time.clock()
gpu_id = 7
detector = MtcnnDetector(caffe_model_path, gpu_id)
t2 = time.clock()
print("initFaceDetector() costs %f seconds" % (t2 - t1))
ttl_time = 0.0
img_cnt = 0
for line in fp:
img_path = line.strip()
print("\n===>" + img_path)
if img_path == '':
print 'empty line, not a file name, skip to next'
continue
if img_path[0] == '#':
print 'skip line starts with #, skip to next'
continue
img_path = osp.join(root_dir, img_path)
print("\nfull path: " + img_path)
rlt = {}
rlt["filename"] = img_path
rlt["faces"] = []
rlt['face_count'] = 0
try:
img = cv2.imread(img_path)
except:
print('failed to load image: ' + img_path)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
if img is None:
print('failed to load image: ' + img_path)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
img_cnt += 1
t1 = time.clock()
img_h = img.shape[0]
img_w = img.shape[1]
max_line = 0
resize_scale = 0
new_img_h = img_h
new_img_w = img_w
if img_h >= img_w:
max_line = img_h
if max_line > MAX_LINE:
resize_scale = max_line / MAX_LINE
new_img_h = MAX_LINE
new_img_w = img_w / resize_scale
if img_w > img_h:
max_line = img_w
if max_line > MAX_LINE:
resize_scale = max_line / MAX_LINE
new_img_w = MAX_LINE
new_img_h = img_h / resize_scale
img = cv2.resize(img, (new_img_w, new_img_h))
bboxes, points = detector.detect_face(img, minsize, threshold,
scale_factor)
t2 = time.clock()
ttl_time += t2 - t1
print("detect_face() costs %f seconds" % (t2 - t1))
if bboxes is not None and len(bboxes) > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4], 'score': box[4], 'pts': pts}
rlt['faces'].append(tmp)
rlt['face_count'] = len(bboxes)
rlt['message'] = 'success'
if SAVE_INTO_SEPERATE_JSON:
fn_json = osp.join(
save_dir,
osp.splitext(osp.basename(img_path))[0] + '.json')
fp_json = open(fn_json, 'w')
json.dump(rlt, fp_json, indent=2)
fp_json.close()
# else:
result_list.append(rlt)
# print('output bboxes: ' + str(bboxes))
# print('output points: ' + str(points))
# toc()
print(
"\n===> Processed %d images, costs %f seconds, avg time: %f seconds"
% (img_cnt, ttl_time, ttl_time / img_cnt))
if bboxes is None:
continue
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(img_path))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
# if not SAVE_INTO_SEPERATE_JSON:
#json.dump(result_list, fp_rlt, indent=2)
fp_rlt.close()
fp.close()
if show_img:
cv2.destroyAllWindows()
def main(img_list_fn, save_dir, save_img=True, show_img=False):
minsize = 20
caffe_model_path = "./model"
threshold = [0.6, 0.7, 0.7]
scale_factor = 0.709
if not osp.exists(save_dir):
os.makedirs(save_dir)
fp_rlt = open(osp.join(save_dir, 'mtcnn_fd_rlt.json'), 'w')
result_list = []
t1 = time.clock()
detector = MtcnnDetector(caffe_model_path)
t2 = time.clock()
print("initFaceDetector() costs %f seconds" % (t2 - t1))
fp = open(img_list_fn, 'r')
ttl_time = 0.0
img_cnt = 0
for line in fp:
imgpath = line.strip()
print("\n===>" + imgpath)
if imgpath == '':
print 'empty line, not a file name, skip to next'
continue
if imgpath[0] == '#':
print 'skip line starts with #, skip to next'
continue
rlt = {}
rlt["filename"] = imgpath
rlt["faces"] = []
rlt['face_count'] = 0
try:
img = cv2.imread(imgpath)
except:
print('failed to load image: ' + imgpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
if img is None:
print('failed to load image: ' + imgpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
continue
img_cnt += 1
t1 = time.clock()
bboxes, points = detector.detect_face(img, minsize, threshold,
scale_factor)
t2 = time.clock()
ttl_time += t2 - t1
print("detect_face() costs %f seconds" % (t2 - t1))
if bboxes is not None and len(bboxes) > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4], 'score': box[4], 'pts': pts}
rlt['faces'].append(tmp)
rlt['face_count'] = len(bboxes)
rlt['message'] = 'success'
result_list.append(rlt)
# print('output bboxes: ' + str(bboxes))
# print('output points: ' + str(points))
# toc()
print(
"\n===> Processed %d images, costs %f seconds, avg time: %f seconds"
% (img_cnt, ttl_time, ttl_time / img_cnt))
if bboxes is None:
continue
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(imgpath))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
json.dump(result_list, fp_rlt, indent=4)
fp_rlt.close()
fp.close()
if show_img:
cv2.destroyAllWindows()
def main(vggface_list_fn,
save_dir,
save_img=False,
show_img=False):
minsize = 20
caffe_model_path = "../model"
threshold = [0.6, 0.7, 0.7]
scale_factor = 0.709
if not osp.exists(save_dir):
os.makedirs(save_dir)
#fp_rlt = open(osp.join(save_dir, 'vggface_mtcnn_fd_rlt.json'), 'w')
#result_list = []
t1 = time.clock()
detector = MtcnnDetector(caffe_model_path)
t2 = time.clock()
print("initFaceDetector() costs %f seconds" % (t2 - t1))
#fp = open(lfw_list_fn, 'r')
ttl_time = 0.0
img_cnt = 0
fp = open(vggface_list_fn,'r')
all_lines = fp.readlines()
count = 1
#lines = all_lines[:100]
for line in all_lines:
data = json.loads(line)
print count
count = count + 1
#img_cnt +=1
gt = data[u'label'][u'detect'][u'general_d'][u'bbox'][0][u'pts']
pose = data[u'label'][u'detect'][u'general_d'][u'bbox'][0][u'pose']
isExists=os.path.exists('./dataset/vgg_face_dataset/vgg_face/%s' % data[u'url'].split('/')[-2])
if not isExists:
os.makedirs('./dataset/vgg_face_dataset/vgg_face/%s' % data[u'url'].split('/')[-2])
# urllib.urlretrieve(data[u'url'],'./dataset/vgg_face_dataset/vgg_face/%s/%s' % (data[u'url'].split('/')[-2],data[u'url'].split('/')[-1]))
#print data[u'url'].split('/')[-1][:-4]
result_list = []
resultpath = '%s/%s' % (data[u'url'].split('/')[-2],data[u'url'].split('/')[-1][:-4])
result_name = resultpath + '.json'
imgpath = './dataset/vgg_face_dataset/vgg_face/%s/%s' % (data[u'url'].split('/')[-2],data[u'url'].split('/')[-1])
#print("\n===>" + imgpath)
isExists2=os.path.exists('./result_json/%s' % data[u'url'].split('/')[-2])
isExists3=os.path.exists(osp.join(save_dir,result_name))
if not isExists2:
os.makedirs('./result_json/%s' % data[u'url'].split('/')[-2])
if isExists3:
continue
print("\n===>" + imgpath)
fp_rlt = open(osp.join(save_dir,result_name),'w')
#id = 'unkown' if len(splits) < 2 else splits[1]
id = data[u'facecluster']
rlt = {}
rlt["filename"] = imgpath
rlt["faces"] = []
rlt['face_count'] = 0
rlt['id'] = id
rlt['gt'] = gt
rlt['pose'] = pose
try:
img = io.imread(data[u'url'])
#print "img.shape",img.shape
except:
print('failed to load image: ' + imgpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
json.dump(result_list,fp_rlt,indent=4)
fp_rlt.close()
continue
#if img is None:
if len(img.shape) != 3:
print('failed to load image: ' + imgpath)
rlt["message"] = "failed to load"
result_list.append(rlt)
json.dump(result_list,fp_rlt,indent=4)
fp_rlt.close()
continue
img_cnt += 1
t1 = time.clock()
bboxes, points = detector.detect_face(img, minsize,
threshold, scale_factor)
t2 = time.clock()
ttl_time += t2 - t1
print("detect_face() costs %f seconds" % (t2 - t1))
if len(bboxes) > 0:
for (box, pts) in zip(bboxes, points):
# box = box.tolist()
# pts = pts.tolist()
tmp = {'rect': box[0:4],
'score': box[4],
'pts': pts
}
rlt['faces'].append(tmp)
rlt['face_count'] = len(bboxes)
rlt['message'] = 'success'
result_list.append(rlt)
# print('output bboxes: ' + str(bboxes))
# print('output points: ' + str(points))
# toc()
if bboxes is None:
json.dump(result_list,fp_rlt,indent=4)
fp_rlt.close()
continue
print("\n===> Processed %d images, costs %f seconds, avg time: %f seconds" % (
img_cnt, ttl_time, ttl_time / img_cnt))
if save_img or show_img:
draw_faces(img, bboxes, points)
if save_img:
save_name = osp.join(save_dir, osp.basename(imgpath))
cv2.imwrite(save_name, img)
if show_img:
cv2.imshow('img', img)
ch = cv2.waitKey(0) & 0xFF
if ch == 27:
break
json.dump(result_list, fp_rlt, indent=4)
fp_rlt.close()
fp.close()
if show_img:
cv2.destroyAllWindows()