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| from fastapi import FastAPI, File, UploadFile, HTTPException | |
| from PIL import Image | |
| import numpy as np | |
| import pickle | |
| from io import BytesIO | |
| import math | |
| app = FastAPI() | |
| som = load_model() | |
| MM = np.array([ | |
| [ 0., -1., -1., -1., -1., 2., -1., -1., -1., 3.], | |
| [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], | |
| [-1., -1., -1., 1., -1., -1., -1., -1., -1., -1.], | |
| [ 1., -1., -1., -1., -1., -1., -1., -1., -1., 0.], | |
| [-1., -1., -1., -1., 1., -1., -1., -1., -1., -1.], | |
| [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], | |
| [ 3., -1., -1., -1., -1., -1., -1., -1., -1., 3.], | |
| [-1., -1., -1., 0., -1., -1., 3., -1., -1., -1.], | |
| [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], | |
| [ 2., -1., -1., -1., 1., -1., -1., -1., -1., 2.] | |
| ]) | |
| async def predict_fingerprint_api(file: UploadFile = File(...)): | |
| try: | |
| contents = await file.read() | |
| image = Image.open(BytesIO(contents)).convert('L') | |
| image = np.asarray(image) | |
| print(f"ARRAY{image.size}:\n\n\n{image}") | |
| image = np.array(image.array).reshape(256, 256, 1) | |
| representative_data = representativo(image) | |
| representative_data = representative_data.reshape(1, -1) | |
| w = som.winner(representative_data) | |
| prediction = MM[w] | |
| return {"prediction": prediction} | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| def load_model(): | |
| with open('som.pkl', 'rb') as fid: | |
| som = pickle.load(fid) | |
| return som | |
| 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): | |
| 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): | |
| 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:j+1] * Gy[i*w:(i+1)*w, j:j+1])) | |
| XX = sum(sum(Gx[i*w:(i+1)*w, j:j+1]**2 - Gy[i*w:(i+1)*w, j:j+1]**2)) | |
| mOrientaciones[i, j] = (0.5 * math.atan2(YY, XX) + math.pi / 2.0) * (180.0 / math.pi) | |
| return mOrientaciones | |
| def representativo(imarray): | |
| imarray = np.squeeze(imarray) | |
| m, n = imarray.shape | |
| patron = imarray[1:m-1, 1:n-1] | |
| EE = orientacion(patron, 14) | |
| return np.asarray(EE).reshape(-1) |