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27d15d2413
- update datastore - raise meaningful errors while importing optional dependencies - handle unexpected zero dimension issue in alignment
138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
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 OpenCvWrapper
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from deepface.commons import functions
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from deepface.commons.logger import Logger
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logger = Logger(module="detectors.SsdWrapper")
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# pylint: disable=line-too-long
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def build_model() -> dict:
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"""
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Build a ssd detector model
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Returns:
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model (Any)
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"""
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home = functions.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 os.path.isfile(home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel") != True:
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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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eye_detector = OpenCvWrapper.build_cascade("haarcascade_eye")
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detector = {}
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detector["face_detector"] = face_detector
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detector["eye_detector"] = eye_detector
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return detector
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def detect_face(detector: dict, img: np.ndarray, align: bool = True) -> list:
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"""
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Detect and align face with ssd
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Args:
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face_detector (Any): ssd face detector object
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img (np.ndarray): pre-loaded image
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align (bool): default is true
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Returns:
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list of detected and aligned faces
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"""
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resp = []
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detected_face = None
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img_region = [0, 0, img.shape[1], img.shape[0]]
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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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base_img = img.copy() # we will restore base_img to img later
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original_size = img.shape
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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=img)
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face_detector = detector["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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detected_face = base_img[
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int(top * aspect_ratio_y) : int(bottom * aspect_ratio_y),
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int(left * aspect_ratio_x) : int(right * aspect_ratio_x),
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]
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img_region = [
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int(left * aspect_ratio_x),
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int(top * aspect_ratio_y),
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int(right * aspect_ratio_x) - int(left * aspect_ratio_x),
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int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y),
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]
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confidence = instance["confidence"]
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if align:
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detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
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resp.append((detected_face, img_region, confidence))
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return resp
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