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update retinaface_trt del cvtColor and increase THRESH (#535)

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* update retinaface_trt del cvtColor and increase THRESH

* update macro in retinafce_xx.cpp and preprocess_image description

* retinaface_xx.cpp add a space after

* HRESH for CONF and THRESHOLD for IOU is unified --THRESH

Co-authored-by: Admin <huangz11@chinatelecom.cn>
This commit is contained in:
Huang ZhuangZhuang 2021-05-11 19:57:11 +08:00 committed by GitHub
parent 2c4fdea67d
commit 6a74b88b4e
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 13 additions and 12 deletions
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7
retinaface/retina_mnet.cpp
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@ -12,7 +12,8 @@
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define BATCH_SIZE 1
#define VIS_THRESH 0.6
#define CONF_THRESH = 0.75
#define IOU_THRESH = 0.4
// stuff we know about the network and the input/output blobs
static const int INPUT_H = decodeplugin::INPUT_H; // H, W must be able to be divided by 32.
@ -349,13 +350,13 @@ int main(int argc, char** argv) {
for (int b = 0; b < BATCH_SIZE; b++) {
std::vector<decodeplugin::Detection> res;
nms(res, &prob[b * OUTPUT_SIZE]);
nms(res, &prob[b * OUTPUT_SIZE], IOU_THRESH);
std::cout << "number of detections -> " << prob[b * OUTPUT_SIZE] << std::endl;
std::cout << " -> " << prob[b * OUTPUT_SIZE + 10] << std::endl;
std::cout << "after nms -> " << res.size() << std::endl;
cv::Mat tmp = img.clone();
for (size_t j = 0; j < res.size(); j++) {
if (res[j].class_confidence < VIS_THRESH) continue;
if (res[j].class_confidence < CONF_THRESH) continue;
cv::Rect r = get_rect_adapt_landmark(tmp, INPUT_W, INPUT_H, res[j].bbox, res[j].landmark);
cv::rectangle(tmp, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
//cv::putText(tmp, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);

6
retinaface/retina_r50.cpp
View File

@ -12,6 +12,8 @@
#define USE_INT8 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define BATCH_SIZE 1
#define CONF_THRESH = 0.75
#define IOU_THRESH = 0.4
// stuff we know about the network and the input/output blobs
static const int INPUT_H = decodeplugin::INPUT_H; // H, W must be able to be divided by 32.
@ -369,13 +371,13 @@ int main(int argc, char** argv) {
for (int b = 0; b < BATCH_SIZE; b++) {
std::vector<decodeplugin::Detection> res;
nms(res, &prob[b * OUTPUT_SIZE]);
nms(res, &prob[b * OUTPUT_SIZE], IOU_THRESH);
std::cout << "number of detections -> " << prob[b * OUTPUT_SIZE] << std::endl;
std::cout << " -> " << prob[b * OUTPUT_SIZE + 10] << std::endl;
std::cout << "after nms -> " << res.size() << std::endl;
cv::Mat tmp = img.clone();
for (size_t j = 0; j < res.size(); j++) {
if (res[j].class_confidence < 0.1) continue;
if (res[j].class_confidence < CONF_THRESH) continue;
cv::Rect r = get_rect_adapt_landmark(tmp, INPUT_W, INPUT_H, res[j].bbox, res[j].landmark);
cv::rectangle(tmp, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
//cv::putText(tmp, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);

12
retinaface/retinaface_trt.py
View File

@ -19,8 +19,8 @@ import torchvision
INPUT_H = 480 #defined in decode.h
INPUT_W = 640
CONF_THRESH = 0.4
IOU_THRESHOLD = 0.1
CONF_THRESH = 0.75
IOU_THRESHOLD = 0.4
np.set_printoptions(threshold=np.inf)
def plot_one_box(x, landmark,img, color=None, label=None, line_thickness=None):
@ -180,9 +180,8 @@ class Retinaface_trt(object):
def preprocess_image(self, input_image_path):
"""
description: Read an image from image path, convert it to RGB,
resize and pad it to target size, normalize to [0,1],
transform to NCHW format.
description: Read an image from image path, resize and pad it to target size,
normalize to [0,1],transform to NCHW format.
param:
input_image_path: str, image path
return:
@ -193,8 +192,7 @@ class Retinaface_trt(object):
"""
image_raw = cv2.imread(input_image_path)
h, w, c = image_raw.shape
image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (INPUT_W, INPUT_H))
image = cv2.resize(image_raw, (INPUT_W, INPUT_H))
image = image.astype(np.float32)