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| from huggingface_hub import from_pretrained_fastai | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| import os | |
| import shutil | |
| from os import path | |
| from zipfile import ZipFile | |
| from shutil import make_archive | |
| import imutils | |
| import time | |
| import dlib | |
| import cv2 | |
| from google.colab.patches import cv2_imshow | |
| import os | |
| from imutils import paths | |
| import numpy as np | |
| import pickle | |
| import vptree | |
| args = {} | |
| # VP tree output file | |
| args["tree"] = "tree.pickle" | |
| # Hash dictionary output file | |
| args["hashes"] = "hashesCaras.pickle" | |
| # Hamming distance threshold | |
| args["distance"] = 10 | |
| # Hallar el rectangulo con la cara | |
| def convert_and_trim_bb(image, rect): | |
| # extract the starting and ending (x, y)-coordinates of the | |
| # bounding box | |
| startX = rect.left() | |
| startY = rect.top() | |
| endX = rect.right() | |
| endY = rect.bottom() | |
| # ensure the bounding box coordinates fall within the spatial | |
| # dimensions of the image | |
| startX = max(0, startX) | |
| startY = max(0, startY) | |
| endX = min(endX, image.shape[1]) | |
| endY = min(endY, image.shape[0]) | |
| # compute the width and height of the bounding box | |
| w = endX - startX | |
| h = endY - startY | |
| # return our bounding box coordinates | |
| return (startX, startY, w, h) | |
| detector = dlib.get_frontal_face_detector() | |
| # Initial image is modified | |
| # final hash size is hashSize^2 bits | |
| def dhash(image, hashSize=8): | |
| # Convert to gray | |
| if (image.shape[2] == 3): | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Resize to hashSize (add 1 column for gradient calculations) | |
| resized = cv2.resize(image, (hashSize + 1, hashSize)) | |
| # Compute relative horizontal gradient in columns | |
| diff = resized[:, 1:] > resized[:, :-1] | |
| # convert the difference image to a hash | |
| return sum([2 ** i for (i, v) in enumerate(diff.flatten()) if v]) | |
| def convert_hash(h): | |
| # Convert hash "h" into python's int | |
| return int(np.array(h, dtype="float64")) | |
| def hamming(a, b): | |
| # Hamming distance | |
| # Count 1 at XOR | |
| return bin(int(a) ^ int(b)).count("1") | |
| tree = pickle.loads(open(args["tree"], "rb").read()) | |
| hashes = pickle.loads(open(args["hashes"], "rb").read()) | |
| def FaceDetect(img): | |
| # Get hashes | |
| scannedhashes = [] | |
| # Scan faces | |
| image = imutils.resize(image, width=600) | |
| rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| upsample=3 | |
| rects = detector(rgb, upsample) | |
| # Discard if no detection | |
| if len(rects)==0: | |
| print("Me cago en tu puta madre") | |
| else: | |
| boxes = [convert_and_trim_bb(image, r) for r in rects] | |
| for (x, y, w, h) in boxes: | |
| cara = image[y:y+h, x:x+w] | |
| cv2_imshow(cara) | |
| # Compute the hash for the face and convert it | |
| try: | |
| h = dhash(cara.copy()) | |
| except: | |
| continue | |
| h = convert_hash(h) | |
| # Update the nice hashes dictionary | |
| scannedhashes.append(h) | |
| for i in scannedhashes: | |
| results = tree.get_all_in_range(i, args["distance"]) | |
| results = sorted(results) | |
| resultPathsList = [] | |
| for (d,h) in results: | |
| resultPaths = hashes.get(h, []) | |
| if len(resultPaths) == 0: | |
| continue | |
| for p in resultPaths: | |
| if p not in resultPathsList: | |
| resultPathsList.append(p) | |
| for p in resultPathsList: | |
| print(p) | |
| # Creamos la interfaz y la lanzamos. | |
| gr.Interface(fn=FaceDetect, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['Pawn.jpg','Knight.jpg']).launch(share=False) | |