init

.gitattributes

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+somhuella.pkl filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.12.7
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir -r /code/requirements.txt
+RUN pip install fastapi uvicorn
+
+COPY . .
+
+RUN chmod -R 777 /code
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from PIL import Image
+import numpy as np
+from io import BytesIO
+import math
+import pickle
+import os
+
+app = FastAPI(title="Fingerprint detection API")
+
+som_model = None
+classification_matrix = None
+
+def sobel(I):
+    m, n = I.shape
+    Gx = np.zeros([m-2, n-2], np.float32)
+    Gy = np.zeros([m-2, n-2], np.float32)
+    gx = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
+    gy = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]
+
+    for j in range(1, m-2):
+        for i in range(1, n-2):
+            Gx[j-1, i-1] = sum(sum(I[j-1:j+2, i-1:i+2] * gx))
+            Gy[j-1, i-1] = sum(sum(I[j-1:j+2, i-1:i+2] * gy))
+
+    return Gx, Gy
+
+def medfilt2(G, d=3):
+    m, n = G.shape
+    temp = np.zeros([m+2*(d//2), n+2*(d//2)], np.float32)
+    salida = np.zeros([m, n], np.float32)
+    temp[1:m+1, 1:n+1] = G
+
+    for i in range(1, m):
+        for j in range(1, n):
+            A = np.asarray(temp[i-1:i+2, j-1:j+2]).reshape(-1)
+            salida[i-1, j-1] = np.sort(A)[d+1]
+
+    return salida
+
+def orientacion(patron, w):
+    Gx, Gy = sobel(patron)
+    Gx = medfilt2(Gx)
+    Gy = medfilt2(Gy)
+    m, n = Gx.shape
+    mOrientaciones = np.zeros([m//w, n//w], np.float32)
+
+    for i in range(m//w):
+        for j in range(n//w):
+            YY = sum(sum(2*Gx[i*w:(i+1)*w, j*w:(j+1)*w]*Gy[i*w:(i+1)*w, j*w:(j+1)*w]))
+            XX = sum(sum(Gx[i*w:(i+1)*w, j*w:(j+1)*w]**2-Gy[i*w:(i+1)*w, j*w:(j+1)*w]**2))
+            mOrientaciones[i, j] = (0.5*math.atan2(YY, XX) + math.pi/2.0)*(180.0/math.pi)
+
+    return mOrientaciones
+
+def representativo(image_array):
+    if isinstance(image_array, np.ndarray):
+        if len(image_array.shape) == 3:
+            image_array = np.mean(image_array, axis=2)
+        im = Image.fromarray(image_array.astype(np.uint8))
+    else:
+        im = image_array
+
+    im = im.resize((256, 256))
+    m, n = im.size
+    imarray = np.array(im, np.float32)
+    patron = imarray[1:m-1, 1:n-1]
+    EE = orientacion(patron, 14)
+
+    return np.asarray(EE).reshape(-1)
+
+def is_valid_fingerprint_features(features):
+    if features is None or len(features) != 324:
+        return False
+
+    if np.any(np.isnan(features)) or np.any(np.isinf(features)):
+        return False
+
+    if np.min(features) < 0 or np.max(features) > 180:
+        return False
+
+    orientation_variance = np.var(features)
+    if orientation_variance < 100:
+        return False
+
+    bins = np.histogram(features, bins=18, range=(0, 180))[0]
+    non_empty_bins = np.sum(bins > 0)
+
+    if non_empty_bins < 6:
+        return False
+
+    return True
+
+def load_trained_model():
+    global som_model, classification_matrix
+
+    try:
+        if os.path.exists('somhuella.pkl'):
+            with open('somhuella.pkl', 'rb') as f:
+                som_model = pickle.load(f)
+            print("OK model")
+        else:
+            print("cargar somhuella.pkl")
+            return False
+
+        if os.path.exists('matrizMM.txt'):
+            classification_matrix = np.loadtxt('matrizMM.txt')
+            print("OK matrix")
+        else:
+            print("load matrix")
+            return False
+
+        return True
+    except Exception as e:
+        print(f"error {e}")
+        return False
+
+def detect_and_classify_fingerprint(features):
+    global som_model, classification_matrix
+
+    if not is_valid_fingerprint_features(features):
+        return False, "no fingerprint patterns"
+
+    if som_model is None or classification_matrix is None:
+        return True, "Fingerprint pattern detected"
+
+    try:
+        winner = som_model.winner(features)
+
+        classification_value = classification_matrix[winner]
+        if classification_value == -1:
+            return False, "no pattern"
+
+        class_names = {
+            0: "LEFT_LOOP",
+            1: "RIGHT_LOOP", 
+            2: "WHORL",
+            3: "ARCO"
+        }
+
+        class_name = class_names.get(int(classification_value), "UNKNOWN")
+
+        return True, f"{class_name} fingerprint detected (class {int(classification_value)})"
+
+    except Exception as e:
+        print(f"Error in SOM class: {e}")
+        return True, "Fingerprint pattern detected (classification error)"
+
+load_trained_model()
+
+@app.post("/detect_fingerprint/")
+async def detect_fingerprint(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(BytesIO(contents))
+        image_array = np.array(image)
+        features = representativo(image_array)
+        is_fingerprint, details = detect_and_classify_fingerprint(features)
+
+        return {
+            "fingerprint_detected": is_fingerprint,
+            "details": details
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"error processing image: {str(e)}")

matrizMM.txt

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+-1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+2.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 0.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+-1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+-1.000000000000000000e+00 -1.000000000000000000e+00 1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 0.000000000000000000e+00
+-1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 2.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+3.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+-1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 3.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+-1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00
+3.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 1.000000000000000000e+00 -1.000000000000000000e+00 -1.000000000000000000e+00 2.000000000000000000e+00

requirements.txt

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+fastapi[standard]
+uvicorn[standard]
+python-multipart
+Pillow
+numpy
+minisom

somhuella.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4ec5da7c0d90219b85de5d191eeb0356c5d7ffba9310d979872378b48893f90
+size 268015