File size: 4,108 Bytes
41de6ea d77c00f 41de6ea 1590197 41de6ea 06be3c9 d77c00f 06be3c9 41de6ea 06be3c9 41de6ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import numpy as np
import cv2
import joblib
import base64
import io
from flask import Flask, request, render_template
from PIL import Image
from skimage.feature import local_binary_pattern
app = Flask(__name__)
try:
model = joblib.load("svm_fruit_model.pkl")
pca = joblib.load("pca_transformer.pkl")
except Exception as e:
print(f"Error loading model: {e}")
CLASS_NAMES = [
"Avocado",
"Avocado ripe",
"Banana Lady",
"Banana Red",
"Banana Yellow",
"Carambula",
"Cherimoya",
"Dates",
"Fig",
"Guava",
"Kaki",
"Kiwi",
"Lychee",
"Mango",
"Mango Red",
"Mangostan",
"Papaya",
"Pineapple",
"Pineapple Mini",
"Pomegranate",
"Quince",
"Rambutan",
"Salak",
]
def extract_color_histogram(img, bins=(8, 8, 8)):
hist = cv2.calcHist([img], [0, 1, 2], None, bins, [0, 256, 0, 256, 0, 256])
hist = cv2.normalize(hist, hist).flatten()
return hist
def extract_lbp_features(gray_img, P=8, R=1, method="uniform"):
lbp = local_binary_pattern(gray_img, P, R, method)
hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, P + 3), range=(0, P + 2))
hist = hist.astype("float")
hist /= hist.sum() + 1e-6
return hist
def preprocessing_pipeline(pil_img):
img = np.array(pil_img.convert('RGB'))
img = cv2.resize(img, (100, 100), interpolation=cv2.INTER_AREA)
img_float = img.astype(np.float32) / 255.0
img_uint8 = (img_float * 255).astype(np.uint8)
feat_color = extract_color_histogram(img_uint8)
img_gray = cv2.cvtColor(img_uint8, cv2.COLOR_RGB2GRAY)
feat_lbp = extract_lbp_features(img_gray)
combined = np.hstack([feat_color, feat_lbp])
combined = combined.reshape(1, -1)
final_features = pca.transform(combined)
return final_features
@app.route("/", methods=["GET", "POST"])
def index():
prediction_text = None
img_data = None
confidence = None
top_3_predictions = None
if request.method == "POST":
if "file" not in request.files:
return render_template("index.html", msg="Tidak ada file")
file = request.files["file"]
if file.filename == "":
return render_template("index.html", msg="Nama file kosong")
if file:
try:
image = Image.open(file.stream)
features = preprocessing_pipeline(image)
preprocessed_img = cv2.resize(np.array(image.convert('RGB')), (100, 100), interpolation=cv2.INTER_AREA)
preprocessed_pil = Image.fromarray(preprocessed_img)
img_io = io.BytesIO()
preprocessed_pil.save(img_io, "PNG")
encoded_img = base64.b64encode(img_io.getvalue()).decode("ascii")
img_data = f"data:image/png;base64, {encoded_img}"
pred_index = model.predict(features)[0]
prediction_text = CLASS_NAMES[int(pred_index)]
if hasattr(model, 'predict_proba'):
probabilities = model.predict_proba(features)[0]
confidence = float(probabilities[int(pred_index)]) * 100
top_3_indices = probabilities.argsort()[-3:][::-1]
top_3_predictions = [
{
'name': CLASS_NAMES[idx],
'probability': float(probabilities[idx]) * 100
}
for idx in top_3_indices
]
else:
confidence = None
top_3_predictions = None
except Exception as e:
prediction_text = f"Error: {str(e)}"
confidence = None
top_3_predictions = None
return render_template("index.html", prediction=prediction_text, img_data=img_data,
confidence=confidence, top_predictions=top_3_predictions)
if __name__ == "__main__":
app.run(debug=True, port=7860)
|