def run_predict():
predictor = CustomImagePrediction()
predictor.setModelPath(model_path="trafficnetmodelacc-0.893750.h5")
predictor.setJsonPath(model_json="model_class.json")
predictor.loadFullModel(num_objects=4)
predictions, probabilities = predictor.predictImage(image_input="images/dense.jpg", result_count=4)
for prediction, probability in zip(predictions, probabilities):
print(prediction, " : ", probability)
def run_predict():
predictor = CustomImagePrediction()
predictor.setModelPath(model_path="trafficnet_resnet_model_ex-055_acc-0.913750.h5")
predictor.setJsonPath(model_json="model_class.json")
predictor.loadFullModel(num_objects=4)
predictions, probabilities = predictor.predictImage(image_input="", result_count=4) #PATH FOR IMAGE
for prediction, probability in zip(predictions, probabilities):
print(prediction, " : ", probability)
def run_predict():
predictor = CustomImagePrediction()
predictor.setModelPath(model_path="action_net_ex-060_acc-0.745313.h5")
predictor.setJsonPath(model_json="model_class.json")
predictor.loadFullModel(num_objects=16)
predictions, probabilities = predictor.predictImage(
image_input="images/5.jpg", result_count=4)
for prediction, probability in zip(predictions, probabilities):
print(prediction, " : ", probability)
def test_custom_recognition_full_model_resnet():
predictor = CustomImagePrediction()
predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
predictor.loadFullModel(num_objects=10)
predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "9.jpg"))
assert isinstance(predictions, list)
assert isinstance(probabilities, list)
assert isinstance(predictions[0], str)
assert isinstance(probabilities[0], str)
def activity_detector(image_name):
result = []
predictor = CustomImagePrediction()
predictor.setModelPath(
model_path=os.path.join(root, "action-detection-image", "action.h5"))
predictor.setJsonPath(model_json=os.path.join(
root, "action-detection-image", "model_class.json"))
predictor.loadFullModel(num_objects=16)
predictions, probabilities = predictor.predictImage(image_input=image_name,
result_count=4)
for prediction, probability in zip(predictions, probabilities):
result.append([prediction, probability])
print(result)
return result[0][0]
def test_custom_recognition_full_model_resnet_multi():
try:
keras.backend.clear_session()
except:
None
predictor = CustomImagePrediction()
predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
predictor.loadFullModel(num_objects=10)
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], str)
def run_predict():
predictor = CustomImagePrediction()
predictor.setModelPath(
model_path="trafficnet_resnet_model_ex-055_acc-0.913750.h5")
predictor.setJsonPath(model_json="model_class.json")
predictor.loadFullModel(num_objects=4)
predictions, probabilities = predictor.predictImage(
image_input="images/traff.jpg", result_count=4)
for prediction, probability in zip(predictions, probabilities):
print(prediction, " : ", probability)
result["accident"][prediction] = probability
# otus thresholding of 80
if probability > 80:
result["accident_result"][prediction] = True
else:
result["accident_result"][prediction] = False
if result["accident_result"]["Accident"] == True or result[
"accident_result"]["Fire"] == True:
sendemail()
print(result)
write(result)
from imageai.Prediction.Custom import CustomImagePrediction
import os
# Load the model to use for prediction
predictor = CustomImagePrediction()
predictor.setModelPath(model_path="model_ex-035_acc-0.874667.h5")
predictor.setJsonPath(model_json="model_class.json")
predictor.loadFullModel(num_objects=5)
directory = "C:/Programs/Java/Computer-Vision-Team19/testing/"
ints = []
# Put the names of the image files into the array of ints
for file in os.listdir(directory):
filename = os.fsdecode(file)
if filename.endswith(".jpg"):
ints.append(int(filename.split(".", 1)[0]))
else:
print("Ignoring file named " + filename)
# Sort the filenames and print a newline
ints.sort()
print()
# Log prediction results to a file
log = open("../run3.txt", "w")
for i in ints:
filename = str(i) + ".jpg"
path = os.path.join(directory, filename)
prediction, probability = predictor.predictImage(image_input=path,
def pre():
execution_path = os.getcwd()
im_path = execution_path #+ "/imx"
os.system("rm log/" + "*.txt")
#note please copy the name of the model that you want to use from /content/im6/models
################################################
##m_n="best-models/eye16-ResNet/model_ex-055_acc-0.992638.h5"
##m_n="best-models/eye8-DenseNet/model_ex-055_acc-0.992402.h5"
#m_n="best-models/eye16-SqueezeNet/model_ex-001_acc-0.494081.h5"
m_n = "best-models/eye16-InceptionV3/model_ex-022_acc-0.992049.h5"
################################################
from imageai.Prediction.Custom import CustomImagePrediction
prediction = CustomImagePrediction()
prediction.setModelPath(model_path=im_path + "/" + m_n)
prediction.setJsonPath(model_json=im_path +
"/best-models/model_class.json")
prediction.loadFullModel(num_objects=2)
pa = im_path + "/pic/"
print("==================\n==== Welcome ====\n==================")
while True:
output = []
t1 = time.time()
uuu = -1
########################################
sss = ""
for file in sorted(os.listdir(pa)):
#if file.endswith(".jpg"):
try:
uuu = uuu + 1
image_path = pa + "/" + file
t1x = time.time()
results, probabilities = prediction.predictImage(
image_input=image_path, result_count=1)
t2x = time.time()
print("Time-->", t2x - t1x)
output.append(results)
if results[0] == 'close':
x = "1"
else:
x = "0"
sss = sss + x
f = open('log/log.txt', 'a+')
f.write("%s" % x)
f.close()
os.system("rm " + image_path)
print(x)
except:
continue
########################################
'''for file in sorted(os.listdir(pa)):
#if file.endswith(".jpg"):
try:
uuu=uuu+1
image_path=pa+"/"+file
results, probabilities = prediction.predictImage(image_input=image_path, result_count=1)
output.append(results)
os.system("rm "+image_path)
#print(image_path)
except:
continue
sss=""
with open('log/log.txt', 'a+') as f:
for item in output:
if item[0]=='close':
x="1"
else:
x="0"
sss=sss+x
f.write("%s" % x)'''
t2 = time.time()
if uuu > 0:
print("Time-->", t2 - t1, "Files#", uuu)
predictor = CustomImagePrediction()
predictor.setModelPath(
model_path=os.path.join(execution_path, "idenprof_resnet.h5")
) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
predictor.setJsonPath(model_json=os.path.join(execution_path, "idenprof.json"))
predictor.setModelTypeAsResNet()
predictor.loadModel(num_objects=10)
predictor2 = CustomImagePrediction()
predictor2.setModelPath(
model_path=os.path.join(execution_path,
"idenprof_full_resnet_ex-001_acc-0.119792.h5")
) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
predictor2.setJsonPath(
model_json=os.path.join(execution_path, "idenprof.json"))
predictor2.loadFullModel(num_objects=10)
predictor3 = CustomImagePrediction()
predictor3.setModelPath(
model_path=os.path.join(execution_path, "idenprof_inception_0.719500.h5")
) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
predictor3.setJsonPath(
model_json=os.path.join(execution_path, "idenprof.json"))
predictor3.setModelTypeAsInceptionV3()
predictor3.loadModel(num_objects=10)
results, probabilities = predictor.predictImage(image_input=os.path.join(
execution_path, "9.jpg"),
result_count=5)
print(results)
print(probabilities)
