This is the official github repository of Orangelib.. Orangelib is a library built to simplify the implementation of computer vision in real problems. It is a library for classifying oranges, apples and bananas.
The models for classifying oranges, bananas and apples are trained with MobilleNetV2. Both the trained models and the dataset used in training the models are available as releases in this repository.
Install Orangelib with:
pip install orangelib
Upgrade Orangelib with:
pip install orangelib --upgrade
Orangeclassifier is used to classify ripe and unripe oranges.
from orangelib.model import OrangeClassifier
classifier = OrangeClassifier("trained_model.h5")
fruit_name, confidence = classifier.predict("path_to_image")
print(" Fruit Name: ",fruit_name)
print("Prediction Confidence: ",confidence)
Looking into each line of code:
from orangelib.model import OrangeClassifier
We import in the class for classifying oranges from orangelib
classifier = OrangeClassifier("trained_model.h5")
The path to model used for classifying oranges is loaded.
fruit_name, confidence = classifier.predict("path_to_image")
The path to image to be predicted is loaded
print(" Fruit Name: ",fruit_name)
print("Prediction Confidence: ",confidence)
The fruit name and the level of confidence of the class predicted are printed out
orange_sample1
fruit_name, confidence = classifier.predict("photos/sample1.jpg")
output1
Fruit Name: unripe orange
Prediction Confidence: 99.92031
orange_sample2
fruit_name, confidence = classifier.predict("photos/sample2.jpg")
output2
Fruit Name: ripe orange
Prediction Confidence: 99.99995
orange_sample3
fruit_name, confidence = classifier.predict("photos/sample3.jpg")
output3
Fruit Name: ripe orange
Prediction Confidence: 99.99991 0.9999149
orange_sample4
fruit_name, confidence = classifier.predict("photos/sample4.jpg")
output4
Fruit Name: unripe orange
Prediction Confidence: 99.99999
We may not to stress ourselves predicting a single image, when we intend to predict multiple images.
Code for implementing multiple predictions with Orangelib
from orangelib.model import OrangeClassifier
classifier = OrangeClassifier("orange_model.h5")
fruit_names_list, confidence_list = classifier.predictBatch(["sample1.jpg","sample2.jpg","sample3.jpg", "sample4.jpg"])
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
fruit_names_list, confidence_list = classifier.predictBatch(["sample1.jpg","sample2.jpg","sample3.jpg", "sample4.jpg"])
We perform predictions on an array of images using the predictBatch function
There is no limit to the number of images that can be predicted using the predictBatch function
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
We loop through the array of predictions and print it out the predictions for each of the images.
Fruit Name: unripe orange
Prediction Confidence: 99.92031
Fruit Name: ripe orange
Prediction Confidence: 99.99995
Fruit Name: ripe orange
Prediction Confidence: 99.99991
Fruit Name: unripe orange
Prediction Confidence: 99.99999
It gives the same level of predictions for the images just as when they were predicted individually.
We are able to classify ripe and unripe oranges with over 99percent accuracy.
Bananaclassifier is used to classify ripe and unripe bananas.
banana_sample1
from orangelib.model import BananaClassifierClassifier
classifier = BananaClassifier("banana_model.h5")
fruit_name, confidence = classifier.predict("bananas/sample5.jpg")
print(" Fruit Name: ",fruit_name)
print("Prediction Confidence: ",confidence)
Little modifications to the code are:
We import in the class BananaClassifier from Orangelib and load the trained banana model.
Output1
Fruit Name: ripe banana
Prediction Confidence: 99.99983
banana_sample2
fruit_name, confidence = classifier.predict("bananas/sample6.jpg")
output2
Fruit Name: unripe banana
Prediction Confidence: 99.87182
banana_sample3
fruit_name, confidence = classifier.predict("bananas/sample7.jpg")
output3
Fruit Name: ripe banana
Prediction Confidence: 99.99490
banana_sample4
fruit_name, confidence = classifier.predict("bananas/sample8.jpg")
output4
Fruit Name: unripe banana
Prediction Confidence: 99.99660
Code for implementing multiple predictions with Orangelib Using BananaClassifier
from orangelib.model import BananaClassifier
classifier = OrangeClassifier("banana_model.h5")
fruit_names_list, confidence_list = classifier.predictBatch(["banana/sample5.jpg","bananas/sample6.jpg","bananas/sample7.jpg", "banana/sample8.jpg"])
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
fruit_names_list, confidence_list = classifier.predictBatch(["bananas/sample5.jpg","bananas/sample6.jpg","bananas/sample7.jpg", "bananas/sample8.jpg"])
We perform predictions on an array of images using the predictBatch function.
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
We loop through the array of predictions and print it out the predictions for each of the images.
Fruit Name: ripe banana
Prediction Confidence: 99.99983
Fruit Name: unripe banana
Prediction Confidence: 99.87182
Fruit Name: ripe banana
Prediction Confidence: 99.99490
Fruit Name: unripe banana
Prediction Confidence: 99.99660
It gives the same level of predictions for the images just as when they were predicted individually.
We are able to classify ripe and unripe bananas with over 99percent accuracy.
Appleclassifier is used to classify green and red apples.
apple_sample1
from orangelib.model import AppleClassifier
classifier = AppleClassifier("apple_model.h5")
fruit_name, confidence = classifier.predict("apples/sample3.jpg")
print(" Fruit Name: ",fruit_name)
print("Prediction Confidence: ",confidence)
Little modifications to the code are: We import in the class AppleClassifier from Orangelib and load the trained apple model.
Output1
Fruit Name: green apple
Prediction Confidence: 99.94303
apple_sample2
fruit_name, confidence = classifier.predict("apples/sample6.jpg")
output2
Fruit Name: red apple
Prediction Confidence: 100.0
apple_sample3
fruit_name, confidence = classifier.predict("apples/sample7.jpg")
output3
Fruit Name: green apple
Prediction Confidence: 99.88158
apple_sample4
fruit_name, confidence = classifier.predict("apples/sample8.jpg")
output4
FruitName:red apple
Prediction Confidence: 100.0
Code for implementing multiple predictions with Orangelib Using AppleClassifier
from orangelib.model import AppleClassifier
classifier = AppleClassifier("apple_model.h5")
fruit_names_list, confidence_list = classifier.predictBatch(["apples/sample3.jpg","apples/sample6.jpg","apples/sample7.jpg", "apples/sample8.jpg"])
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
fruit_names_list, confidence_list = classifier.predictBatch(["bananas/sample5.jpg","bananas/sample6.jpg","bananas/sample7.jpg", "bananas/sample8.jpg"])
We perform predictions on an array of images using predictBatch function
for fruit_names, confidence in zip(fruit_names_list,confidence_list):
print("Fruit Name: ",fruit_names)
print("Prediction Confidence: ", confidence)
We loop through the array of predictions and print it out the predictions for each of the images.
Fruit Name: green apple
Prediction Confidence: 99.91635084152222
Fruit Name: red apple
Prediction Confidence: 100.0
Fruit Name: green apple
Prediction Confidence: 99.88158941268921
FruitName:red apple
Prediction Confidence: 100.0
It gives the same level of predictions for the images just as when they were predicted individually.
We are able to classify green apples with over 99percent surprisingly! It classified the red apples with 100% accuracy.
Install Orangelib and test it with many images of oranges, bananas and apples.
-
Building an Image Recognition Model for Mobile using Depthwise Convolutions. https://heartbeat.fritz.ai/building-an-image-recognition-model-for-mobile-using-depthwise-convolutions-643d70e0f7e2
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MobileNetV2: Inverted Residuals and Linear Bottlenecks. https://arxiv.org/abs/1801.04381
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