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https://github.com/serengil/deepface.git
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105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
import gdown
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from pathlib import Path
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import os
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import cv2
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import pandas as pd
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from deepface.detectors import OpenCvWrapper
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from deepface.commons import functions
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def build_model():
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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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print("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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print("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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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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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, img, align = True):
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resp = []
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detected_face = None
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img_region = [0, 0, img.shape[0], img.shape[1]]
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confidence = 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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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 index, 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[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)]
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img_region = [int(left*aspect_ratio_x), int(top*aspect_ratio_y), int(right*aspect_ratio_x) - int(left*aspect_ratio_x), int(bottom*aspect_ratio_y) - int(top*aspect_ratio_y)]
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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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