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https://github.com/serengil/deepface.git
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154 lines
5.1 KiB
Python
154 lines
5.1 KiB
Python
from typing import List
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import os
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import gdown
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import cv2
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import pandas as pd
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import numpy as np
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from deepface.detectors import OpenCv
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from deepface.commons import folder_utils
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from deepface.models.Detector import Detector, FacialAreaRegion
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from deepface.commons.logger import Logger
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logger = Logger()
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# pylint: disable=line-too-long, c-extension-no-member
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class SsdClient(Detector):
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def __init__(self):
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self.model = self.build_model()
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def build_model(self) -> dict:
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"""
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Build a ssd detector model
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Returns:
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model (dict)
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"""
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home = folder_utils.get_deepface_home()
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# model structure
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if os.path.isfile(home + "/.deepface/weights/deploy.prototxt") != True:
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logger.info("deploy.prototxt will be downloaded...")
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url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
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output = home + "/.deepface/weights/deploy.prototxt"
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gdown.download(url, output, quiet=False)
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# pre-trained weights
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if (
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os.path.isfile(home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
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!= True
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):
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logger.info("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
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url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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output = home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
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gdown.download(url, output, quiet=False)
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try:
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face_detector = cv2.dnn.readNetFromCaffe(
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home + "/.deepface/weights/deploy.prototxt",
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home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel",
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)
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except Exception as err:
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raise ValueError(
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"Exception while calling opencv.dnn module."
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+ "This is an optional dependency."
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+ "You can install it as pip install opencv-contrib-python."
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) from err
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detector = {}
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detector["face_detector"] = face_detector
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detector["opencv_module"] = OpenCv.OpenCvClient()
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return detector
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def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
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"""
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Detect and align face with ssd
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Args:
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img (np.ndarray): pre-loaded image as numpy array
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Returns:
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results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
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"""
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opencv_module: OpenCv.OpenCvClient = self.model["opencv_module"]
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resp = []
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detected_face = None
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ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"]
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target_size = (300, 300)
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original_size = img.shape
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current_img = cv2.resize(img, target_size)
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aspect_ratio_x = original_size[1] / target_size[1]
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aspect_ratio_y = original_size[0] / target_size[0]
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imageBlob = cv2.dnn.blobFromImage(image=current_img)
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face_detector = self.model["face_detector"]
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face_detector.setInput(imageBlob)
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detections = face_detector.forward()
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detections_df = pd.DataFrame(detections[0][0], columns=ssd_labels)
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detections_df = detections_df[detections_df["is_face"] == 1] # 0: background, 1: face
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detections_df = detections_df[detections_df["confidence"] >= 0.90]
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detections_df["left"] = (detections_df["left"] * 300).astype(int)
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detections_df["bottom"] = (detections_df["bottom"] * 300).astype(int)
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detections_df["right"] = (detections_df["right"] * 300).astype(int)
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detections_df["top"] = (detections_df["top"] * 300).astype(int)
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if detections_df.shape[0] > 0:
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for _, instance in detections_df.iterrows():
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left = instance["left"]
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right = instance["right"]
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bottom = instance["bottom"]
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top = instance["top"]
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confidence = instance["confidence"]
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x = int(left * aspect_ratio_x)
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y = int(top * aspect_ratio_y)
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w = int(right * aspect_ratio_x) - int(left * aspect_ratio_x)
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h = int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y)
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detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
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left_eye, right_eye = opencv_module.find_eyes(detected_face)
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# eyes found in the detected face instead image itself
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# detected face's coordinates should be added
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if left_eye is not None:
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left_eye = (int(x + left_eye[0]), int(y + left_eye[1]))
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if right_eye is not None:
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right_eye = (int(x + right_eye[0]), int(y + right_eye[1]))
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facial_area = FacialAreaRegion(
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x=x,
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y=y,
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w=w,
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h=h,
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left_eye=left_eye,
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right_eye=right_eye,
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confidence=confidence,
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)
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resp.append(facial_area)
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return resp
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