import argparse import cv2 import numpy as np import torch import kornia as K from kornia.contrib import FaceDetector, FaceDetectorResult import gradio as gr import face_detection def compare_detect_faces(img: np.ndarray, confidence_threshold, nms_threshold, kornia_toggle, retina_toggle, retina_mobile_toggle, dsfd_toggle ): detections = [] if kornia_toggle=="On": kornia_detections = kornia_detect(img, confidence_threshold=confidence_threshold, nms_threshold=nms_threshold) else: kornia_detections = None if retina_toggle=="On": retina_detections = retina_detect(img, confidence_threshold=confidence_threshold, nms_threshold=nms_threshold) detections.append(retina_detections) else: retina_detections = None if retina_mobile_toggle=="On": retina_mobile_detections = retina_mobilenet_detect(img, confidence_threshold=confidence_threshold, nms_threshold=nms_threshold) detections.append(retina_mobile_detections) else: retina_mobile_detections = None if dsfd_toggle=="On": dsfd_detections = dsfd_detect(img, confidence_threshold=confidence_threshold, nms_threshold=nms_threshold) detections.append(dsfd_detections) else: dsfd_detections = None return kornia_detections, retina_detections, retina_mobile_detections, dsfd_detections def scale_image(img: np.ndarray, size: int) -> np.ndarray: h, w = img.shape[:2] scale = 1.0 * size / w return cv2.resize(img, (int(w * scale), int(h * scale))) def base_detect(detector, img): img = scale_image(img, 400) detections = detector.detect(img) img_vis = img.copy() for box in detections: img_vis = cv2.rectangle(img_vis, box[:2].astype(int).tolist(), box[2:4].astype(int).tolist(), (0, 255, 0), 1) return img_vis def retina_detect(img, confidence_threshold, nms_threshold): detector = face_detection.build_detector( "RetinaNetResNet50", confidence_threshold=confidence_threshold, nms_iou_threshold=nms_threshold) img_vis = base_detect(detector, img) return img_vis def retina_mobilenet_detect(img, confidence_threshold, nms_threshold): detector = face_detection.build_detector( "RetinaNetMobileNetV1", confidence_threshold=confidence_threshold, nms_iou_threshold=nms_threshold) img_vis = base_detect(detector, img) return img_vis def dsfd_detect(img, confidence_threshold, nms_threshold): detector = face_detection.build_detector( "DSFDDetector", confidence_threshold=confidence_threshold, nms_iou_threshold=nms_threshold) img_vis = base_detect(detector, img) return img_vis def kornia_detect(img, confidence_threshold, nms_threshold): # select the device device = torch.device('cpu') # load the image and scale img_raw = scale_image(img, 400) # preprocess img = K.image_to_tensor(img_raw, keepdim=False).to(device) img = K.color.bgr_to_rgb(img.float()) # create the detector and find the faces ! face_detection = FaceDetector(confidence_threshold=confidence_threshold, nms_threshold=nms_threshold).to(device) with torch.no_grad(): dets = face_detection(img) dets = [FaceDetectorResult(o) for o in dets[0]] # show image img_vis = img_raw.copy() for b in dets: # draw face bounding box img_vis = cv2.rectangle(img_vis, b.top_left.int().tolist(), b.bottom_right.int().tolist(), (0, 255, 0), 1) return img_vis input_image = gr.components.Image() image_kornia = gr.components.Image(label="Kornia YuNet") image_retina = gr.components.Image(label="RetinaFace") image_retina_mobile = gr.components.Image(label="Retina Mobilenet") image_dsfd = gr.components.Image(label="DSFD") confidence_slider = gr.components.Slider(minimum=0.1, maximum=0.95, value=0.5, step=0.05, label="Confidence Threshold") nms_slider = gr.components.Slider(minimum=0.1, maximum=0.95, value=0.3, step=0.05, label="Non Maximum Supression (NMS) Threshold") kornia_radio = gr.Radio(["On", "Off"], value="On", label="Kornia YuNet") retinanet_radio = gr.Radio(["On", "Off"], value="On", label="RetinaFace") retina_mobile_radio = gr.Radio(["On", "Off"], value="On", label="Retina Mobilenets") dsfd_radio = gr.Radio(["On", "Off"], value="On", label="DSFD") #methods_dropdown = gr.components.Dropdown(["Kornia YuNet", "RetinaFace", "RetinaMobile", "DSFD"], value="Kornia YuNet", label="Choose a method") description = """This space let's you compare different face detection algorithms, based on Convolutional Neural Networks (CNNs). The models used here are: * Kornia YuNet: High Speed. Using the [Kornia Face Detection](https://kornia.readthedocs.io/en/latest/applications/face_detection.html) implementation * RetinaFace: High Accuracy. Using the [RetinaFace](https://arxiv.org/pdf/1905.00641.pdf) implementation with ResNet50 backbone from the [face-detection library](https://github.com/hukkelas/DSFD-Pytorch-Inference) * RetinaMobileNet: Mid Speed, Mid Accuracy. RetinaFace with a MobileNetV1 backbone, also from the [face-detection library](https://github.com/hukkelas/DSFD-Pytorch-Inference) * DSFD: High Accuracy. [Dual Shot Face Detector](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_DSFD_Dual_Shot_Face_Detector_CVPR_2019_paper.pdf) from the [face-detection library](https://github.com/hukkelas/DSFD-Pytorch-Inference) as well. """ compare_iface = gr.Interface( fn=compare_detect_faces, inputs=[input_image, confidence_slider, nms_slider, kornia_radio, retinanet_radio, retina_mobile_radio, dsfd_radio],#, size_slider, neighbour_slider, scale_slider], outputs=[image_kornia, image_retina, image_retina_mobile, image_dsfd], examples=[["data/50_Celebration_Or_Party_birthdayparty_50_25.jpg", 0.5, 0.3, "On", "On", "On", "On"], ["data/12_Group_Group_12_Group_Group_12_39.jpg", 0.5, 0.3, "On", "On", "On", "On"], ["data/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg", 0.5, 0.3, "On", "On", "On", "On"], ["data/12_Group_Group_12_Group_Group_12_283.jpg", 0.5, 0.3, "On", "On", "On", "On"]], title="Face Detections", description=description ).launch()