Comment obtenir les coordonnées du rectangle englobant dans la détection d'objets YOLO?
J'ai besoin d'obtenir les coordonnées de la boîte englobante générées dans l'image ci-dessus à l'aide de la détection d'objets YOLO.
Une solution rapide consiste à modifier le fichier image.c pour imprimer les informations du cadre de sélection:
...
if(bot > im.h-1) bot = im.h-1;
// Print bounding box values
printf("Bounding Box: Left=%d, Top=%d, Right=%d, Bottom=%d\n", left, top, right, bot);
draw_box_width(im, left, top, right, bot, width, red, green, blue);
...
Il y a un joli petit python (2 - mais avec de petites modifications 3. [changez simplement l'impression et les chaînes en chaînes binaires dans le main ]) programme que vous pouvez utiliser dans le référentiel principal https://github.com/pjreddie/darknet/blob/master/python/darknet.py
REMARQUE! Les coordonnées données sont le milieu et la largeur et la hauteur.
pour python utilisateur dans Windows:
d'abord ..., faites plusieurs travaux de réglage:
paramètre python chemin de votre dossier darknet dans le chemin de l'environnement:
PYTHONPATH = 'YOUR DARKNET FOLDER'ajoutez PYTHONPATH à la valeur Path en ajoutant:
%PYTHONPATH%modifier le fichier
coco.datadanscfg folder, en remplaçant la variable de dossiernamespar votrecoco.namesdossier, dans mon cas:names = D:/core/darknetAB/data/coco.names
avec ce paramètre, vous pouvez appeler darknet.py (depuis le référentiel alexeyAB\darknet ) en tant que module python à partir de n'importe quel dossier).
démarrer le script:
from darknet import performDetect as scan #calling 'performDetect' function from darknet.py
def detect(str):
''' this script if you want only want get the coord '''
picpath = str
cfg='D:/core/darknetAB/cfg/yolov3.cfg' #change this if you want use different config
coco='D:/core/darknetAB/cfg/coco.data' #you can change this too
data='D:/core/darknetAB/yolov3.weights' #and this, can be change by you
test = scan(imagePath=picpath, thresh=0.25, configPath=cfg, weightPath=data, metaPath=coco, showImage=False, makeImageOnly=False, initOnly=False) #default format, i prefer only call the result not to produce image to get more performance
#until here you will get some data in default mode from alexeyAB, as explain in module.
#try to: help(scan), explain about the result format of process is: [(item_name, convidence_rate (x_center_image, y_center_image, width_size_box, height_size_of_box))],
#to change it with generally used form, like PIL/opencv, do like this below (still in detect function that we create):
newdata = []
if len(test) >=2:
for x in test:
item, confidence_rate, imagedata = x
x1, y1, w_size, h_size = imagedata
x_start = round(x1 - (weight_size/2))
y_start = round(y1 - (height_size/2))
x_end = round(x_start + w_size)
y_end = round(y_start + h_size)
data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
newdata.append(data)
Elif len(test) == 1:
item, confidence_rate, imagedata = test
x1, y1, w_size, h_size = imagedata
x_start = round(x1 - (w_size/2))
y_start = round(y1 - (h_size/2))
x_end = round(x_start + w_size)
y_end = round(y_start + h_size)
data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
newdata.append(data)
else:
newdata = False
return newdata
Comment l'utiliser:
table = 'D:/test/image/test1.jpg'
checking = detect(table)'
pour obtenir les coordonnées:
si seulement 1 résultat:
x1, y1, x2, y2 = checking[2]
si plusieurs résultent:
for x in checking:
item = x[0]
x1, y1, x2, y2 = x[2]
print(item)
print(x1, y1, x2, y2)
Si vous allez l'implémenter dans python, il y a ce petit wrapper python que j'ai créé dans ici . Suivez le fichier ReadMe et installez-le. Ce sera très facile à installer.
Ensuite, suivez ceci exemple de code pour savoir comment détecter des objets.
Si votre détection est det
top_left_x = det.bbox.x
top_left_y = det.bbox.y
width = det.bbox.w
height = det.bbox.h
Si vous en avez besoin, vous pouvez obtenir le point médian en:
mid_x, mid_y = det.bbox.get_point(pyyolo.BBox.Location.MID)
J'espère que cela t'aides..
Inspiré de la réponse @Wahyu ci-dessus. Il y a peu de changements, modifications et corrections de bugs et testés avec la détection d'un seul objet et la détection d'objets multiples.
# calling 'performDetect' function from darknet.py
from darknet import performDetect as scan
import math
def detect(img_path):
''' this script if you want only want get the coord '''
picpath = img_path
# change this if you want use different config
cfg = '/home/saggi/Documents/saggi/prabin/darknet/cfg/yolo-obj.cfg'
coco = '/home/saggi/Documents/saggi/prabin/darknet/obj.data' # you can change this too
# and this, can be change by you
data = '/home/saggi/Documents/saggi/prabin/darknet/backup/yolo-obj_last.weights'
test = scan(imagePath=picpath, thresh=0.25, configPath=cfg, weightPath=data, metaPath=coco, showImage=False, makeImageOnly=False,
initOnly=False) # default format, i prefer only call the result not to produce image to get more performance
# until here you will get some data in default mode from alexeyAB, as explain in module.
# try to: help(scan), explain about the result format of process is: [(item_name, convidence_rate (x_center_image, y_center_image, width_size_box, height_size_of_box))],
# to change it with generally used form, like PIL/opencv, do like this below (still in detect function that we create):
newdata = []
# For multiple Detection
if len(test) >= 2:
for x in test:
item, confidence_rate, imagedata = x
x1, y1, w_size, h_size = imagedata
x_start = round(x1 - (w_size/2))
y_start = round(y1 - (h_size/2))
x_end = round(x_start + w_size)
y_end = round(y_start + h_size)
data = (item, confidence_rate,
(x_start, y_start, x_end, y_end), (w_size, h_size))
newdata.append(data)
# For Single Detection
Elif len(test) == 1:
item, confidence_rate, imagedata = test[0]
x1, y1, w_size, h_size = imagedata
x_start = round(x1 - (w_size/2))
y_start = round(y1 - (h_size/2))
x_end = round(x_start + w_size)
y_end = round(y_start + h_size)
data = (item, confidence_rate,
(x_start, y_start, x_end, y_end), (w_size, h_size))
newdata.append(data)
else:
newdata = False
return newdata
if __name__ == "__main__":
# Multiple detection image test
# table = '/home/saggi/Documents/saggi/prabin/darknet/data/26.jpg'
# Single detection image test
table = '/home/saggi/Documents/saggi/prabin/darknet/data/1.jpg'
detections = detect(table)
# Multiple detection
if len(detections) > 1:
for detection in detections:
print(' ')
print('========================================================')
print(' ')
print('All Parameter of Detection: ', detection)
print(' ')
print('========================================================')
print(' ')
print('Detected label: ', detection[0])
print(' ')
print('========================================================')
print(' ')
print('Detected object Confidence: ', detection[1])
x1, y1, x2, y2 = detection[2]
print(' ')
print('========================================================')
print(' ')
print(
'Detected object top left and bottom right cordinates (x1,y1,x2,y2): x1, y1, x2, y2')
print('x1: ', x1)
print('y1: ', y1)
print('x2: ', x2)
print('y2: ', y2)
print(' ')
print('========================================================')
print(' ')
print('Detected object width and height: ', detection[3])
b_width, b_height = detection[3]
print('Weidth of bounding box: ', math.ceil(b_width))
print('Height of bounding box: ', math.ceil(b_height))
print(' ')
print('========================================================')
# Single detection
else:
print(' ')
print('========================================================')
print(' ')
print('All Parameter of Detection: ', detections)
print(' ')
print('========================================================')
print(' ')
print('Detected label: ', detections[0][0])
print(' ')
print('========================================================')
print(' ')
print('Detected object Confidence: ', detections[0][1])
x1, y1, x2, y2 = detections[0][2]
print(' ')
print('========================================================')
print(' ')
print(
'Detected object top left and bottom right cordinates (x1,y1,x2,y2): x1, y1, x2, y2')
print('x1: ', x1)
print('y1: ', y1)
print('x2: ', x2)
print('y2: ', y2)
print(' ')
print('========================================================')
print(' ')
print('Detected object width and height: ', detections[0][3])
b_width, b_height = detections[0][3]
print('Weidth of bounding box: ', math.ceil(b_width))
print('Height of bounding box: ', math.ceil(b_height))
print(' ')
print('========================================================')
# Single detections output:
# test value [('movie_name', 0.9223029017448425, (206.79859924316406, 245.4672393798828, 384.83673095703125, 72.8630142211914))]
# Multiple detections output:
# test value [('movie_name', 0.9225175976753235, (92.47076416015625, 224.9121551513672, 147.2491912841797, 42.063255310058594)),
# ('movie_name', 0.4900225102901459, (90.5261459350586, 12.4061279296875, 182.5990447998047, 21.261077880859375))]