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Feature_Extraction_Test.py
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Feature_Extraction_Test.py
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import numpy
import skimage.io, skimage.color, skimage.feature
import os
import pickle
fruits = ["apple", "lemon", "mango", "raspberry", "Dates", "Banana"]
# 164+166+166+166+166+166=994
dataset_features = numpy.zeros(shape=(994, 360))
outputs = numpy.zeros(shape=994)
idx = 0
class_label = 0
for fruit_dir in fruits:
curr_dir = os.path.join(os.path.sep + "Datasets/Fruits-360/Test", fruit_dir)
all_imgs = os.listdir(os.getcwd()+curr_dir)
for img_file in all_imgs:
if img_file.endswith(".jpg"): # Ensures reading only JPG files.
fruit_data = skimage.io.imread(fname=os.path.sep.join([os.getcwd(), curr_dir , img_file]), as_gray=False)
fruit_data_hsv = skimage.color.rgb2hsv(rgb=fruit_data)
hist = numpy.histogram(a=fruit_data_hsv[:, :, 0], bins=360)
dataset_features[idx, :] = hist[0]
outputs[idx] = class_label
idx = idx + 1
class_label = class_label + 1
with open("Datasets/test_set_features_new.pkl", "wb") as f:
pickle.dump(dataset_features, f)
with open("Datasets/test_set_labels_new.pkl", "wb") as f:
pickle.dump(outputs, f)