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import numpy as np
import cv2
from faster_rcnn_wrapper import FasterRCNNSlim
import tensorflow as tf
import argparse
import os
import json
import time
from nms_wrapper import NMSType, NMSWrapper
def detect(sess, rcnn_cls, image):
# pre-processing image for Faster-RCNN
img_origin = image.astype(np.float32, copy=True)
img_origin -= np.array([[[102.9801, 115.9465, 112.7717]]])
img_shape = img_origin.shape
img_size_min = np.min(img_shape[:2])
img_size_max = np.max(img_shape[:2])
img_scale = 600 / img_size_min
if np.round(img_scale * img_size_max) > 1000:
img_scale = 1000 / img_size_max
img = cv2.resize(img_origin, None, None, img_scale, img_scale, cv2.INTER_LINEAR)
img_info = np.array([img.shape[0], img.shape[1], img_scale], dtype=np.float32)
img = np.expand_dims(img, 0)
# test image
_, scores, bbox_pred, rois = rcnn_cls.test_image(sess, img, img_info)
# bbox transform
boxes = rois[:, 1:] / img_scale
boxes = boxes.astype(bbox_pred.dtype, copy=False)
widths = boxes[:, 2] - boxes[:, 0] + 1
heights = boxes[:, 3] - boxes[:, 1] + 1
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
dx = bbox_pred[:, 0::4]
dy = bbox_pred[:, 1::4]
dw = bbox_pred[:, 2::4]
dh = bbox_pred[:, 3::4]
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
pred_w = np.exp(dw) * widths[:, np.newaxis]
pred_h = np.exp(dh) * heights[:, np.newaxis]
pred_boxes = np.zeros_like(bbox_pred, dtype=bbox_pred.dtype)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
# clipping edge
pred_boxes[:, 0::4] = np.maximum(pred_boxes[:, 0::4], 0)
pred_boxes[:, 1::4] = np.maximum(pred_boxes[:, 1::4], 0)
pred_boxes[:, 2::4] = np.minimum(pred_boxes[:, 2::4], img_shape[1] - 1)
pred_boxes[:, 3::4] = np.minimum(pred_boxes[:, 3::4], img_shape[0] - 1)
return scores, pred_boxes
def load_file_from_dir(dir_path):
ret = []
for file in os.listdir(dir_path):
path_comb = os.path.join(dir_path, file)
if os.path.isdir(path_comb):
ret += load_file_from_dir(path_comb)
else:
ret.append(path_comb)
return ret
def fmt_time(dtime):
if dtime <= 0:
return '0:00.000'
elif dtime < 60:
return '0:%02d.%03d' % (int(dtime), int(dtime * 1000) % 1000)
elif dtime < 3600:
return '%d:%02d.%03d' % (int(dtime / 60), int(dtime) % 60, int(dtime * 1000) % 1000)
else:
return '%d:%02d:%02d.%03d' % (int(dtime / 3600), int((dtime % 3600) / 60), int(dtime) % 60,
int(dtime * 1000) % 1000)
def main():
parser = argparse.ArgumentParser(description='Anime face detector demo')
parser.add_argument('-i', help='The input path of an image or directory', required=True, dest='input', type=str)
parser.add_argument('-o', help='The output json path of the detection result', dest='output')
parser.add_argument('-nms', help='Change the threshold for non maximum suppression',
dest='nms_thresh', default=0.3, type=float)
parser.add_argument('-conf', help='Change the threshold for class regression', dest='conf_thresh',
default=0.8, type=float)
parser.add_argument('-model', help='Specify a new path for model', dest='model', type=str,
default='model/res101_faster_rcnn_iter_60000.ckpt')
parser.add_argument('-nms-type', help='Type of nms', choices=['PY_NMS', 'CPU_NMS', 'GPU_NMS'], dest='nms_type',
default='CPU_NMS')
args = parser.parse_args()
assert os.path.exists(args.input), 'The input path does not exists'
if os.path.isdir(args.input):
files = load_file_from_dir(args.input)
else:
files = [args.input]
file_len = len(files)
if args.nms_type == 'PY_NMS':
nms_type = NMSType.PY_NMS
elif args.nms_type == 'CPU_NMS':
nms_type = NMSType.CPU_NMS
elif args.nms_type == 'GPU_NMS':
nms_type = NMSType.GPU_NMS
else:
raise ValueError('Incorrect NMS Type, not supported yet')
nms = NMSWrapper(nms_type)
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.Session(config=cfg)
net = FasterRCNNSlim()
saver = tf.train.Saver()
saver.restore(sess, args.model)
result = {}
time_start = time.time()
for idx, file in enumerate(files):
elapsed = time.time() - time_start
eta = (file_len - idx) * elapsed / idx if idx > 0 else 0
print('[%d/%d] Elapsed: %s, ETA: %s >> %s' % (idx+1, file_len, fmt_time(elapsed), fmt_time(eta), file))
img = cv2.imread(file)
scores, boxes = detect(sess, net, img)
boxes = boxes[:, 4:8]
scores = scores[:, 1]
keep = nms(np.hstack([boxes, scores[:, np.newaxis]]).astype(np.float32), args.nms_thresh)
boxes = boxes[keep, :]
scores = scores[keep]
inds = np.where(scores >= args.conf_thresh)[0]
scores = scores[inds]
boxes = boxes[inds, :]
result[file] = []
for i in range(scores.shape[0]):
x1, y1, x2, y2 = boxes[i, :].tolist()
new_result = {'score': float(scores[i]),
'bbox': [x1, y1, x2, y2]}
result[file].append(new_result)
if args.output is None:
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
if args.output:
if ((idx+1) % 1000) == 0:
# saving the temporary result
with open(args.output, 'w') as f:
json.dump(result, f)
else:
cv2.imshow(file, img)
if args.output:
with open(args.output, 'w') as f:
json.dump(result, f)
else:
cv2.waitKey()
if __name__ == '__main__':
main()
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