OrgLion-ML
/
darknet
mirror of https://github.com/AlexeyAB/darknet.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Minor fix

This commit is contained in:
AlexeyAB 2020-04-22 20:49:27 +03:00
parent 88f28f7fcc
commit b573eab63f
6 changed files with 92 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

78
build/darknet/x64/darknet.py
View File

@ -63,6 +63,9 @@ class DETECTION(Structure):
("uc", POINTER(c_float)),
("points", c_int)]
class DETNUMPAIR(Structure):
_fields_ = [("num", c_int),
("dets", POINTER(DETECTION))]
class IMAGE(Structure):
_fields_ = [("w", c_int),
@ -161,6 +164,9 @@ make_network_boxes.restype = POINTER(DETECTION)
free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]
free_batch_detections = lib.free_batch_detections
free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int]
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
@ -210,6 +216,11 @@ predict_image_letterbox = lib.network_predict_image_letterbox
predict_image_letterbox.argtypes = [c_void_p, IMAGE]
predict_image_letterbox.restype = POINTER(c_float)
network_predict_batch = lib.network_predict_batch
network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int,
c_float, c_float, POINTER(c_int), c_int, c_int]
network_predict_batch.restype = POINTER(DETNUMPAIR)
def array_to_image(arr):
import numpy as np
# need to return old values to avoid python freeing memory
@ -445,5 +456,72 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo
print("Unable to show image: "+str(e))
return detections
def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
import cv2
import numpy as np
# NB! Image sizes should be the same
# You can change the images, yet, be sure that they have the same width and height
img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg']
image_list = [cv2.imread(k) for k in img_samples]
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
pred_height, pred_width, c = image_list[0].shape
net_width, net_height = (network_width(net), network_height(net))
img_list = []
for custom_image_bgr in image_list:
custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
custom_image = cv2.resize(
custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
custom_image = custom_image.transpose(2, 0, 1)
img_list.append(custom_image)
arr = np.concatenate(img_list, axis=0)
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(net_width, net_height, c, data)
batch_dets = network_predict_batch(net, im, batch_size, pred_width,
pred_height, thresh, hier_thresh, None, 0, 0)
batch_boxes = []
batch_scores = []
batch_classes = []
for b in range(batch_size):
num = batch_dets[b].num
dets = batch_dets[b].dets
if nms:
do_nms_obj(dets, num, meta.classes, nms)
boxes = []
scores = []
classes = []
for i in range(num):
det = dets[i]
score = -1
label = None
for c in range(det.classes):
p = det.prob[c]
if p > score:
score = p
label = c
if score > thresh:
box = det.bbox
left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2,
box.x + box.w / 2, box.y + box.h / 2))
boxes.append((top, left, bottom, right))
scores.append(score)
classes.append(label)
boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0)
cv2.rectangle(image_list[b], (left, top),
(right, bottom), boxColor, 2)
cv2.imwrite(os.path.basename(img_samples[b]),image_list[b])
batch_boxes.append(boxes)
batch_scores.append(scores)
batch_classes.append(classes)
free_batch_detections(batch_dets, batch_size)
return batch_boxes, batch_scores, batch_classes
if __name__ == "__main__":
print(performDetect())
#Uncomment the following line to see batch inference working
#print(performBatchDetect())

4
darknet.py
View File

@ -522,6 +522,6 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath
return batch_boxes, batch_scores, batch_classes
if __name__ == "__main__":
#print(performDetect())
print(performDetect())
#Uncomment the following line to see batch inference working
print(performBatchDetect())
#print(performBatchDetect())

6
include/darknet.h
View File

@ -831,10 +831,10 @@ typedef struct detection{
} detection;
// network.c -batch inference
typedef struct detNumPair {
typedef struct det_num_pair {
int num;
detection *dets;
} detNumPair, *pdetNumPair;
} det_num_pair, *pdet_num_pair;
// matrix.h
typedef struct matrix {
@ -945,7 +945,9 @@ LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh,
LIB_API float *network_predict(network net, float *input);
LIB_API float *network_predict_ptr(network *net, float *input);
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
LIB_API det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter);
LIB_API void free_detections(detection *dets, int n);
LIB_API void free_batch_detections(det_num_pair *det_num_pairs, int n);
LIB_API void fuse_conv_batchnorm(network net);
LIB_API void calculate_binary_weights(network net);
LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename);

0
results/tmp.txt Normal file
View File

4
src/demo.c
View File

@ -322,12 +322,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int
}
while (custom_atomic_load_int(&run_detect_in_thread)) {
if(avg_fps > 200) this_thread_yield();
if(avg_fps > 180) this_thread_yield();
else this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0);
}
if (!benchmark) {
while (custom_atomic_load_int(&run_fetch_in_thread)) {
if (avg_fps > 200) this_thread_yield();
if (avg_fps > 180) this_thread_yield();
else this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0);
}
free_image(det_s);

12
src/network.c
View File

@ -895,12 +895,12 @@ void free_detections(detection *dets, int n)
free(dets);
}
void free_batch_detections(detNumPair *detNumPairs, int n)
void free_batch_detections(det_num_pair *det_num_pairs, int n)
{
int i;
for(i=0; i<n; ++i)
free_detections(detNumPairs[i].dets, detNumPairs[i].num);
free(detNumPairs);
free_detections(det_num_pairs[i].dets, det_num_pairs[i].num);
free(det_num_pairs);
}
// JSON format:
@ -978,14 +978,14 @@ float *network_predict_image(network *net, image im)
return p;
}
detNumPair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
{
network_predict(*net, im.data);
detNumPair *pdets = (struct detNumPair *)calloc(batch_size, sizeof(detNumPair));
det_num_pair *pdets = (struct det_num_pair *)calloc(batch_size, sizeof(det_num_pair));
int num;
for(int batch=0; batch<batch_size; batch++){
detection *dets = make_network_boxes_batch(net, thresh, &num, batch);
fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);
fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);
pdets[batch].num = num;
pdets[batch].dets = dets;
}