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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 import logger as log |
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logger = log.get_singletonish_logger() |
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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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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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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] |
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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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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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