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Merge pull request #308 from sryu1/master
Updated keras and opencv keras snippets attempt
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```python | ||
from keras.models import load_model | ||
from PIL import Image, ImageOps #Install pillow instead of PIL | ||
from keras.models import load_model # TensorFlow is required for Keras to work | ||
from PIL import Image, ImageOps # Install pillow instead of PIL | ||
import numpy as np | ||
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# Disable scientific notation for clarity | ||
np.set_printoptions(suppress=True) | ||
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# Load the model | ||
model = load_model('keras_Model.h5', compile=False) | ||
model = load_model("keras_Model.h5", compile=False) | ||
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# Load the labels | ||
class_names = open('labels.txt', 'r').readlines() | ||
class_names = open("labels.txt", "r").readlines() | ||
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# Create the array of the right shape to feed into the keras model | ||
# The 'length' or number of images you can put into the array is | ||
# determined by the first position in the shape tuple, in this case 1. | ||
# determined by the first position in the shape tuple, in this case 1 | ||
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) | ||
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# Replace this with the path to your image | ||
image = Image.open('<IMAGE_PATH>').convert('RGB') | ||
image = Image.open("<IMAGE_PATH>").convert("RGB") | ||
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#resize the image to a 224x224 with the same strategy as in TM2: | ||
#resizing the image to be at least 224x224 and then cropping from the center | ||
# resizing the image to be at least 224x224 and then cropping from the center | ||
size = (224, 224) | ||
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS) | ||
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#turn the image into a numpy array | ||
# turn the image into a numpy array | ||
image_array = np.asarray(image) | ||
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# Normalize the image | ||
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 | ||
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1 | ||
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# Load the image into the array | ||
data[0] = normalized_image_array | ||
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# run the inference | ||
# Predicts the model | ||
prediction = model.predict(data) | ||
index = np.argmax(prediction) | ||
class_name = class_names[index] | ||
confidence_score = prediction[0][index] | ||
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print('Class:', class_name, end='') | ||
print('Confidence score:', confidence_score) | ||
# Print prediction and confidence score | ||
print("Class:", class_name[2:], end="") | ||
print("Confidence Score:", confidence_score) | ||
``` |
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{ | ||
"dependencies": { | ||
"http-server": "^0.11.1" | ||
"http-server": "^14.1.1" | ||
}, | ||
"scripts": { | ||
"start": "http-server --cors snippets", | ||
"lint": "markdownlint ./markdown ./converter" | ||
}, | ||
"devDependencies": { | ||
"markdownlint-cli": "^0.18.0" | ||
"markdownlint-cli": "^0.32.2" | ||
} | ||
} |
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