def Den():
execution_path = os.getcwd()
prediction = ImagePrediction()
prediction.setModelTypeAsDenseNet()
prediction.setModelPath(os.path.join(execution_path, "/home/guest/Documents/Test1/ImageAI-master/imageai/Prediction/Weights/DenseNet.h5"))
prediction.loadModel()
return prediction
def run(self):
print("预测线程启动")
global PredictionResult
global PredictionModelPath
global PredictionResult
global PredictionSpeed
prediction = ImagePrediction()
PredictionResult.set('')
if PredictionModel.get() == 'SqueezeNet':
print('预测模型选中：SqueezeNet')
prediction.setModelTypeAsSqueezeNet()
elif PredictionModel.get() == 'ResNet50':
print('预测模型选中：ResNet50')
prediction.setModelTypeAsResNet()
elif PredictionModel.get() == 'InceptionV3':
print('预测模型选中：InceptionV3')
prediction.setModelTypeAsInceptionV3()
elif PredictionModel.get() == 'DenseNet121':
print('预测模型选中：DenseNet121')
prediction.setModelTypeAsDenseNet()
PredictionModelPath = prediction_model()
print('模型路径：' + PredictionModelPath)
prediction.setModelPath(PredictionModelPath)
speedindex = SpeedSelector.get()
print('识别速度' + PredictionSpeed[speedindex - 1])
bk.clear_session()
prediction.loadModel(prediction_speed=PredictionSpeed[speedindex - 1])
predictions, probabilities = prediction.predictImage(
imagePath, result_count=CountSelector.get())
for eachPrediction, eachProbability in zip(predictions, probabilities):
PredictionResult.set(PredictionResult.get() + "\n" +
str(eachPrediction) +
zh_cn(str(eachPrediction)) + " : " +
str(eachProbability))
print("预测线程结束")
def predict_objects_densnet(filename):
prediction = ImagePrediction()
prediction.setModelTypeAsDenseNet()
prediction.setModelPath(
os.path.join(os.getcwd(), "models/DenseNet-BC-121-32.h5"))
prediction.loadModel()
print("----------DenseNet--------------")
predictions, probabilities = prediction.predictImage(filename,
result_count=20)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction, " : ", eachProbability)
def test_recognition_model_densenet():
predictor = ImagePrediction()
predictor.setModelTypeAsDenseNet()
predictor.setModelPath(
os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
predictor.loadModel()
predictions, probabilities = predictor.predictImage(
image_input=os.path.join(main_folder, main_folder, "data-images",
"1.jpg"))
assert isinstance(predictions, list)
assert isinstance(probabilities, list)
assert isinstance(predictions[0], str)
assert isinstance(probabilities[0], float)
def func_model(self):
prediction = ImagePrediction()
prediction.setModelTypeAsDenseNet()
prediction.setModelPath(self.model_path)
prediction.loadModel()
time_init = time.time()
predictions, probabilities = prediction.predictImage(self.image_path,
result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
self.data_dict[eachPrediction] = eachProbability
print(eachPrediction + " : " + str(eachProbability))
time2 = time.time()
print('this is：' + max(self.data_dict, key=self.data_dict.get))
print('time：' + str(time2 - time_init) + 's')
def test_recognition_model_densenet():
predictor = ImagePrediction()
predictor.setModelTypeAsDenseNet()
predictor.setModelPath(
os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
predictor.loadModel()
images_to_image_array()
result_array = predictor.predictMultipleImages(
sent_images_array=all_images_array)
assert isinstance(result_array, list)
for result in result_array:
assert "predictions" in result
assert "percentage_probabilities" in result
assert isinstance(result["predictions"], list)
assert isinstance(result["percentage_probabilities"], list)
assert isinstance(result["predictions"][0], str)
assert isinstance(result["percentage_probabilities"][0], float)
import os
from imageai.Detection import ObjectDetection
from imageai.Prediction import ImagePrediction
resNetModel = 'models/DenseNet-BC-121-32.h5'
resNetDetectionModel = 'models/resnet50_coco_best_v2.0.1.h5'
execution_path = os.getcwd()
model_path = os.path.join(execution_path, resNetModel)
detection_model_path = os.path.join(execution_path, resNetDetectionModel)
# Predictions
prediction = ImagePrediction()
prediction.setModelTypeAsDenseNet()
prediction.setModelPath(model_path)
prediction.loadModel()
# Detections
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(detection_model_path)
detector.loadModel()
def predict_types(img):
predictions, probabilities = prediction.predictImage(img.strip(), result_count=5)
return list(zip(predictions, probabilities))
def detect_objects(img):
def Make_Features(df):
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeAsDenseNet()
multiple_prediction.setModelPath(
"../mode/trained_models/DenseNet-BC-121-32.h5")
multiple_prediction.loadModel()
detector = ObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath("../mode/trained_models/yolo.h5")
detector.loadModel()
df['DenseNet'] = df['img'].swifter.apply(
lambda x: multiple_prediction.predictImage(path_dir + x,
result_count=3))
df['Yolo'] = df['img'].swifter.apply(
lambda x: detector.detectObjectsFromImage(
path_dir + x,
output_image_path='./new.jpg',
minimum_percentage_probability=20))
df['DenseNet_obj'] = df['DenseNet'].swifter.apply(
lambda x: x[0][0] + ' and ' + x[0][1] + ' and ' + x[0][2])
df['Yolo_obj'] = df['Yolo'].swifter.apply(
lambda x: ' '.join(word for word in [l['name'] for l in x]), axis=1)
df['Img_txt'] = df['DenseNet_obj'] + ' ' + df['Yolo_obj']
df['palette_color'] = df['img'].swifter.apply(
lambda x: ColorThief(path_dir + x).get_palette(color_count=5))
df['Bluriness'] = [
cv2.Laplacian(cv2.imread(path_dir + x, 0), cv2.CV_64F).var()
for x in df['img']
]
df['Imagedim'] = [
cv2.imread(path_dir + x).flatten().shape[0] for x in df['img']
]
df['Yolo_unique'] = df['Yolo_obj'].swifter.apply(lambda x: len(set(x)))
df['Yolo_N_obj'] = df['Yolo_obj'].swifter.apply(lambda x: len(x))
#print(df.describe() )
# First cross variable between text and image :
df['sim_txt_img_gen'] = df.swifter.apply(
lambda x: nlp(x.text).similarity(nlp(x.DenseNet_obj)), axis=1)
df['sim_txt_img_objs'] = df.swifter.apply(lambda x: nlp(x.text).similarity(
nlp(' and '.join(word[0] for word in x.Yolo_obj))),
axis=1)
# extract dominant colors from image
df['paletCol_1'] = df['palette_color'].swifter.apply(
lambda x: (x[0][0] * 65536 + x[0][1] * 256 + x[0][2]))
df['paletCol_2'] = df['palette_color'].swifter.apply(
lambda x: (x[1][0] * 65536 + x[1][1] * 256 + x[1][2]))
df['paletCol_3'] = df['palette_color'].swifter.apply(
lambda x: (x[2][0] * 65536 + x[2][1] * 256 + x[2][2]))
df['paletCol_4'] = df['palette_color'].swifter.apply(
lambda x: (x[3][0] * 65536 + x[3][1] * 256 + x[3][2]))
df['paletCol_5'] = df['palette_color'].swifter.apply(
lambda x: (x[4][0] * 65536 + x[4][1] * 256 + x[4][2]))
# Get Blurry status
# Get shapes
df['brightness'] = [
cv2.mean(cv2.cvtColor(cv2.imread(path_dir + x), cv2.COLOR_BGR2HSV))[1]
/ 255. for x in df['img']
]
df['Saturation'] = [
cv2.mean(cv2.cvtColor(cv2.imread(path_dir + x), cv2.COLOR_BGR2HSV))[0]
/ 255. for x in df['img']
]
df['ImageValue'] = [
cv2.mean(cv2.cvtColor(cv2.imread(path_dir + x), cv2.COLOR_BGR2HSV))[2]
/ 255. for x in df['img']
]
df['word_count'] = df['text'].swifter.apply(
lambda x: len(str(x).split(" ")))
df['char_count'] = df['text'].str.len()
df['stp_count'] = df['text'].swifter.apply(
lambda x: len([x for x in x.split() if x in stop]))
df['spc_count'] = df['text'].swifter.apply(
lambda x: len([x for x in list(x) if x in special_char]))
df['sentiment_txt'] = df['text'].swifter.apply(lambda x: getsentiment(x))
df['sentiment_img'] = df['DenseNet_obj'].swifter.apply(
lambda x: getsentiment(x))
df['prfn_ftr'] = df['text'].swifter.apply(lambda x: nlp(x)._.is_profane)
df['Quant'] = df['text'].swifter.apply(lambda x: len([
y for y in nlp(x).ents
if str(y.label_) == 'MONEY' or str(y.label_) == 'DATE' or str(y.label_)
== 'TIME' or str(y.label_) == 'PERCENT' or str(y.label_) == 'ORDINAL'
or str(y.label_) == 'CARDINAL' or str(y.label_) == 'QUANTITY'
]))
df['Ent'] = df['text'].swifter.apply(lambda x: len([
y for y in nlp(x).ents
if str(y.label_) == 'PERSON' or str(y.label_) == 'NORP' or str(
y.label_) == 'ORG' or str(y.label_) == 'LOC' or str(y.label_) ==
'GPE' or str(y.label_) == 'WORK_OF_ART' or str(y.label_) == 'EVENT'
]))
df['polarity_scores'] = df['text'].swifter.apply(
lambda x: sid.polarity_scores(x))
df['neg_txt'] = df['polarity_scores'].swifter.apply(lambda x: x['neg'])
df['neu_txt'] = df['polarity_scores'].swifter.apply(lambda x: x['neu'])
df['pos_txt'] = df['polarity_scores'].swifter.apply(lambda x: x['pos'])
df['com_txt'] = df['polarity_scores'].swifter.apply(
lambda x: x['compound'])
#df = df.drop(columns=['DenseNet' ,'DenseNet_obj', 'Yolo' , 'Yolo_obj' , 'palette_color', 'polarity_scores'])
return df
from imageai.Prediction import ImagePrediction
import os
data={}
f=open("E:/DatasetA/label_list.txt").readlines()
for i in f:
zj=i.strip('\n').split('\t')[0]
la=i.strip('\n').split('\t')[1]
data[zj]=la
pred=ImagePrediction()
pred.setModelTypeAsDenseNet()
pred.setModelPath("models/DenseNet-BC-121-32.h5")
pred.loadModel()
train=open("E:/DatasetA/train.txt").readlines()
acc=0
n=0
for index,i in enumerate(train):
n+=1
path=i.strip('\n').split('\t')[0]
label=i.strip('\n').split('\t')[1]
flag=0
predictions, probabilities = pred.predictImage("E:/DatasetA/train/"+path, result_count=1000)
for eachPrediction, eachProbability in zip(predictions, probabilities):
if eachPrediction in data.values():
flag=1
# print(eachPrediction + " : " + eachProbability)
# print("true label",data[label])
if eachPrediction==data[label]:
acc+=1
else:
pass
break
from imageai.Prediction import ImagePrediction
import os
# 获取当前python文件所在的路径
execution_path = os.getcwd()
multiple_prediction = ImagePrediction()
multiple_prediction.setModelTypeAsDenseNet()
multiple_prediction.setModelPath("/Users/zhusheng/WorkSpace/Tmp/dataset/models/DenseNet-BC-121-32.h5")
multiple_prediction.loadModel()
all_image_array = []
all_files = os.listdir("/Users/zhusheng/WorkSpace/Tmp/dataset/mycat/")
for each_file in all_files:
if(each_file.endswith(".jpg") or each_file.endswith(".png")):
file_path = os.path.join("/Users/zhusheng/WorkSpace/Tmp/dataset/mycat/", each_file)
all_image_array.append(file_path)
print(all_image_array)
# 开始预测，一次预测一个图片集合
results_array = multiple_prediction.predictMultipleImages(all_image_array, result_count_per_image=5)
# 打印预测结果
for each_result in results_array:
predictions, percentage_probabilities = each_result["predictions"], each_result["percentage_probabilities"]
for index in range(len(predictions)):
print(predictions[index] , " : ", percentage_probabilities[index])
print("-----------------------")
print('Upload the file and predictions to Box.')
exit()
TOKEN = 'yEUCETgkQSQIitfOBTBHtQKjkRQZ957i'
auth = OAuth2(None, None, access_token=TOKEN)
box = Client(auth)
me = box.user().get()
print('logged in to box as', me.login)
folder = box.folder(FOLDER_ID).get()
print('current folder', folder)
# setup prediction engine
engine = ImagePrediction()
engine.setModelTypeAsDenseNet()
engine.setModelPath('DenseNet-BC-121-32.h5')
engine.loadModel()
# get .jpg files
jpg_files = glob.glob('*.jpg')
for image in jpg_files:
# run image through prediction engine
prediction, probablity = engine.predictImage(image)
prediction_data = {
'prediction1': prediction[0] + ' : ' + probablity[0].astype(str),
'prediction2': prediction[1] + ' : ' + probablity[1].astype(str),
'prediction3': prediction[2] + ' : ' + probablity[2].astype(str),
}
new_file = box.folder(FOLDER_ID).upload(image)
print('uploaded file', new_file)
