def test_recognition_model_resnet():
predictor = ImageClassification()
predictor.setModelTypeAsResNet50()
predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_imagenet_tf.2.0.h5"))
predictor.loadModel()
predictions, probabilities = predictor.classifyImage(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)
class AI:
def __init__(self):
self.execution_path = os.getcwd()
def init_object_detection(self):
#import the model
self.detector = ObjectDetection()
self.detector.setModelTypeAsRetinaNet()
self.detector.setModelPath(
os.path.join(self.execution_path, "resnet50_coco_best_v2.1.0.h5"))
self.detector.loadModel()
def grab_object_tags(self, frame, timestamp):
detections = self.detector.detectObjectsFromImage(
input_image=frame,
output_image_path=frame,
minimum_percentage_probability=30)
tags = []
for eachObject in detections:
t = {
"tag": eachObject["name"],
"probability": eachObject["percentage_probability"],
"location": eachObject["box_points"]
}
tags.append(t)
return {"timestamp": timestamp, "tags": tags}
def init_image_classification(self):
# import the model
self.prediction = ImageClassification()
self.prediction.setModelTypeAsResNet50()
self.prediction.setModelPath(
os.path.join(self.execution_path, "resnet50_imagenet_tf.2.0.h5"))
self.prediction.loadModel()
def grab_image_tags(self, frame, timestamp):
predictions, probabilities = self.prediction.classifyImage(
frame, result_count=10)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction, " : ", eachProbability)
def detectobjectinimage():
path = os.getcwd()
image_prediction = ImageClassification()
image_prediction.setModelTypeAsResNet50()
image_prediction.setModelPath(
os.path.join(path, "resnet50_imagenet_tf.2.0.h5"))
image_prediction.loadModel()
predictions, percentage_probabilities = image_prediction.classifyImage(
os.path.join(path, "/home/akwa/PycharmProjects/remindme/img.jpg"),
result_count=10)
for eachPrediction, eachProbability in zip(predictions,
percentage_probabilities):
print(eachPrediction, " : ", eachProbability)
result = "Cup wasn't detected, hence, user hasn't drank water today"
vampire = 'Raid come to our aid, we are dying!!!'
if eachPrediction == "mosquito_net":
detection_window(vampire)
break
from imageai.Classification import ImageClassification
import os
executionpath = os.getcwd()
print(executionpath)
detection = ImageClassification()
detection.setModelTypeAsResNet50()
detection.setModelPath(executionpath + "/resnet50_imagenet_tf.2.0.h5")
detection.loadModel()
detections, percentageprobabilites = detection.classifyImage("Testimage.jpg",
result_count=5)
for Index in range(len(detections)):
print(detections[Index], ":", percentageprobabilites[Index])
from imageai.Classification import ImageClassification
import os
execution_path = os.getcwd()
prediction = ImageClassification()
prediction.setModelTypeAsResNet50()
prediction.setModelPath(
os.path.join(execution_path, "resnet50_imagenet_tf.2.0.h5")
) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/1.0
prediction.loadModel()
predictions, probabilities = prediction.classifyImage(os.path.join(
execution_path, "1.jpg"),
result_count=10)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction, " : ", eachProbability)
