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ultra_face_caffe_inference.py
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ultra_face_caffe_inference.py
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# coding=utf-8
import argparse
import os
import time
from math import ceil
import caffe
import cv2
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--caffe_prototxt_path', default="model/RFB-320/RFB-320.prototxt", type=str, help='caffe_prototxt_path')
parser.add_argument('--caffe_model_path', default="model/RFB-320/RFB-320.caffemodel", type=str, help='caffe_model_path')
parser.add_argument('--input_size', default="320,240", type=str, help='define network input size,format: width,height')
parser.add_argument('--threshold', default=0.7, type=float, help='score threshold')
parser.add_argument('--imgs_path', default="../MNN/imgs", type=str, help='imgs dir')
parser.add_argument('--results_path', default="results", type=str, help='results dir')
parser.add_argument('--mode', default="cpu", type=str, help='cpu or gpu')
args = parser.parse_args()
if args.mode == "cpu":
caffe.set_mode_cpu()
elif args.mode == "gpu":
caffe.set_mode_gpu()
image_mean = np.array([127, 127, 127])
image_std = 128.0
iou_threshold = 0.3
center_variance = 0.1
size_variance = 0.2
min_boxes = [[10.0, 16.0, 24.0], [32.0, 48.0], [64.0, 96.0], [128.0, 192.0, 256.0]]
strides = [8.0, 16.0, 32.0, 64.0]
def define_img_size(image_size):
shrinkage_list = []
feature_map_w_h_list = []
for size in image_size:
feature_map = [int(ceil(size / stride)) for stride in strides]
feature_map_w_h_list.append(feature_map)
for i in range(0, len(image_size)):
shrinkage_list.append(strides)
priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)
return priors
def generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes):
priors = []
for index in range(0, len(feature_map_list[0])):
scale_w = image_size[0] / shrinkage_list[0][index]
scale_h = image_size[1] / shrinkage_list[1][index]
for j in range(0, feature_map_list[1][index]):
for i in range(0, feature_map_list[0][index]):
x_center = (i + 0.5) / scale_w
y_center = (j + 0.5) / scale_h
for min_box in min_boxes[index]:
w = min_box / image_size[0]
h = min_box / image_size[1]
priors.append([
x_center,
y_center,
w,
h
])
print("priors nums:{}".format(len(priors)))
return np.clip(priors, 0.0, 1.0)
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
indexes = np.argsort(scores)
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :]
def area_of(left_top, right_bottom):
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1]
def iou_of(boxes0, boxes1, eps=1e-5):
overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
overlap_area = area_of(overlap_left_top, overlap_right_bottom)
area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
return overlap_area / (area0 + area1 - overlap_area + eps)
def predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):
boxes = boxes[0]
confidences = confidences[0]
picked_box_probs = []
picked_labels = []
for class_index in range(1, confidences.shape[1]):
probs = confidences[:, class_index]
mask = probs > prob_threshold
probs = probs[mask]
if probs.shape[0] == 0:
continue
subset_boxes = boxes[mask, :]
box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)
box_probs = hard_nms(box_probs,
iou_threshold=iou_threshold,
top_k=top_k,
)
picked_box_probs.append(box_probs)
picked_labels.extend([class_index] * box_probs.shape[0])
if not picked_box_probs:
return np.array([]), np.array([]), np.array([])
picked_box_probs = np.concatenate(picked_box_probs)
picked_box_probs[:, 0] *= width
picked_box_probs[:, 1] *= height
picked_box_probs[:, 2] *= width
picked_box_probs[:, 3] *= height
return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]
def convert_locations_to_boxes(locations, priors, center_variance,
size_variance):
if len(priors.shape) + 1 == len(locations.shape):
priors = np.expand_dims(priors, 0)
return np.concatenate([
locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],
np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]
], axis=len(locations.shape) - 1)
def center_form_to_corner_form(locations):
return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,
locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)
def inference():
net = caffe.Net(args.caffe_prototxt_path, args.caffe_model_path, caffe.TEST)
input_size = [int(v.strip()) for v in args.input_size.split(",")]
witdh = input_size[0]
height = input_size[1]
priors = define_img_size(input_size)
net.blobs['input'].reshape(1, 3, height, witdh)
result_path = args.results_path
imgs_path = args.imgs_path
if not os.path.exists(result_path):
os.makedirs(result_path)
listdir = os.listdir(imgs_path)
for file_path in listdir:
img_path = os.path.join(imgs_path, file_path)
img_ori = cv2.imread(img_path)
tmp_batch = np.zeros([1, 3, height, witdh], dtype=np.float32)
rect = cv2.resize(img_ori, (witdh, height))
rect = cv2.cvtColor(rect, cv2.COLOR_BGR2RGB)
image = (rect - image_mean) / image_std
tmp_batch[0, :, :, :] = image.transpose(2, 0, 1)
net.blobs['input'].data[...] = tmp_batch
time_time = time.time()
scores = net.forward()['scores'][0]
boxes = net.forward()['boxes'][0]
print("inference time: {} s".format(round(time.time() - time_time, 4)))
boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)
scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)
boxes = convert_locations_to_boxes(boxes, priors, center_variance, size_variance)
boxes = center_form_to_corner_form(boxes)
boxes, labels, probs = predict(img_ori.shape[1], img_ori.shape[0], scores, boxes, args.threshold)
for i in range(boxes.shape[0]):
box = boxes[i, :]
cv2.rectangle(img_ori, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
cv2.imwrite(os.path.join(result_path, file_path), img_ori)
print("result_pic is written to {}".format(os.path.join(result_path, file_path)))
cv2.imshow("ultraFace_caffe_py", img_ori)
cv2.waitKey(-1)
cv2.destroyAllWindows()
if __name__ == '__main__':
inference()