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import gradio as gr |
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import pickle |
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import numpy as np |
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import cv2 |
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import imutils |
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from PIL import Image |
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from cv2 import CascadeClassifier |
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from fastai.vision.all import * |
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import os |
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os.system('wget https://www.dropbox.com/s/q2498c2ww9q2yx0/noticiasFotos.zip') |
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os.system('unzip noticiasFotos.zip') |
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def hamming(a, b): |
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return bin(int(a) ^ int(b)).count("1") |
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tree = pickle.loads(open("treeHaar.pickle", "rb").read()) |
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hashes = pickle.loads(open('hashesHaar.pickle', "rb").read()) |
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detector = CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml') |
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def convert_hash(h): |
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return int(np.array(h, dtype="float64")) |
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def dhash(image, hashSize=8): |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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resized=cv2.resize(gray, (hashSize+1, hashSize)) |
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diff = resized[:, 1:] > resized[:, :-1] |
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return sum([2 ** i for (i, v) in enumerate(diff.flatten()) if v]) |
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def face_detector(image): |
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image = np.array(PILImage.create(image)) |
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image = imutils.resize(image, width=500) |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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l=[] |
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rects = detector.detectMultiScale(gray, scaleFactor=1.05, |
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minNeighbors=5, minSize=(30, 30), |
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flags=cv2.CASCADE_SCALE_IMAGE) |
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for (x, y, w, h) in rects: |
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l.append(image[y:y+h, x:x+w]) |
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return l |
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def searchAll(img): |
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sol = [] |
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for (i, face) in enumerate(face_detector(img)): |
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sol = sol + search(face) |
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return sol |
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def search(img, dist=10): |
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queryHash = convert_hash(dhash(img)) |
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treeResults = sorted(tree.get_all_in_range(queryHash, dist)) |
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return [Image.open(hashes.get(h, [])[9:]) for (d, h) in treeResults] |
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gr.Interface(fn=searchAll, inputs=gr.inputs.Image(), outputs=gr.Gallery()).launch(share=False) |
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