def compute_YOLO_output():
"""
Load YOLO model and compute the output :
Possible model : yolov1
"""
path_model ='/media/gonthier/HDD/models/yolo/'
model = 'yolo-full' # Version 1 de YOLO trained on VOC12+07
model = 'yolo-voc' # YOLOv2 VOC 2007+2012 2007
model = 'yolo' #YOLOv2 608x608 COCO trainval
cfg = path_model + model + ".cfg"
weights = path_model + model +".weights"
options = {"model": cfg, "load": weights, "threshold": 0.1,
"gpu" : 1.0}
tfnet = TFNet(options)
imgcv = cv2.imread("loulou.jpg")
result = tfnet.return_predict_gonthier(imgcv) # Return the best prediction
print(result.shape)
#print(result)
if(model=='yolo-full'):
C,B,S = 20,2,7
probs = get_probs(result, C, B, S)
probs_per_classe = np.max(probs,axis=0)
print(CLASSESVOC[np.argmax(probs_per_classe)])
if(model=='yolo-voc'):
C,B= 20,5
H = 13
W = 13
probs = get_probs_v2(result,H,W,C,B)
probs_per_classe = np.max(probs,axis=(0,1,2))
print(CLASSESVOC[np.argmax(probs_per_classe)])
if(model=='yolo'):
C,B= 80,5
H = 19
W = 19
probs = get_probs_v2(result,H,W,C,B)
probs_per_classe = np.max(probs,axis=(0,1,2))
print(CLASSESCOCO[np.argmax(probs_per_classe)])
result = tfnet.return_predict(imgcv) # Return the best prediction
print(result)
def compute_YOLO_Perf_Paintings():
classes_paitings = ['aeroplane','bird','boat','chair','cow','diningtable','dog','horse','sheep','train']
path_to_img = '/media/gonthier/HDD/data/Painting_Dataset/'
database = 'Paintings'
databasetxt = database + '.txt'
df_label = pd.read_csv(databasetxt,sep=",")
df_test = df_label[df_label['set']=='test']
sLength = len(df_test['name_img'])
name_img = df_test['name_img'][0]
i = 0
y_test = np.zeros((sLength,10))
path_model ='/media/gonthier/HDD/models/yolo/'
for model in NETS_Pretrained:
print(model)
if model=='yolo-voc' or model=='yolo-full':
CLASSES = CLASSES_SET['VOC']
elif model=='yolo':
CLASSES = CLASSES_SET['COCO']
nbClasses = len(CLASSES)
cfg = path_model + model + ".cfg"
weights = path_model + model +".weights"
options = {"model": cfg, "load": weights, "threshold": 0.1,
"gpu" : 1.0}
tfnet = TFNet(options)
scores_all_image = np.zeros((len(df_test),nbClasses))
for i,name_img in enumerate(df_test['name_img']):
if i%1000==0:
print(i,name_img)
complet_name = path_to_img + name_img + '.jpg'
im = cv2.imread(complet_name)
result = tfnet.return_predict_gonthier(im) # Arguments: im (ndarray): a color image in BGR order
if(model=='yolo-full'):
C,B,S = 20,2,7
probs = get_probs(result, C, B, S)
probs_per_classe = np.max(probs,axis=0)
if(model=='yolo-voc'):
C,B= 20,5
H = 13
W = 13
probs = get_probs_v2(result,H,W,C,B)
probs_per_classe = np.max(probs,axis=(0,1,2))
if(model=='yolo'):
C,B= 80,5
H = 19
W = 19
probs = get_probs_v2(result,H,W,C,B)
probs_per_classe = np.max(probs,axis=(0,1,2))
scores_all_image[i,:] = probs_per_classe
for j in range(10):
if(classes_paitings[j] in list(df_test['classe'][df_test['name_img']==name_img])[0]):
y_test[i,j] = 1
AP_per_class = []
for k,classe in enumerate(classes_paitings):
index_classe = np.where(np.array(CLASSES)==classe)[0][0]
scores_per_class = scores_all_image[:,index_classe]
#print(scores_per_class)
#print(y_test[:,k],np.sum(y_test[:,k]))
AP = average_precision_score(y_test[:,k],scores_per_class,average=None)
AP_per_class += [AP]
print("Average Precision for",classe," = ",AP)
print(model," mean Average Precision = {0:.3f}".format(np.mean(AP_per_class)))
