init

README.md

@@ -1,6 +1,6 @@
 ---
 title: Face Detection Cnn
-emoji: 📈
+emoji: 👱
 colorFrom: yellow
 colorTo: red
 sdk: gradio

app.py

@@ -0,0 +1,140 @@
+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 detect_faces(img: np.ndarray, method:str):
+    frame = np.array(img) 
+    
+    kornia_detections = kornia_detect(frame)
+    retina_detections = retina_detect(frame)
+    retina_mobile_detections = retina_mobilenet_detect(frame)
+    dsfd_detections = dsfd_detect(frame)
+
+    # if method == "Kornia YuNet":
+    #     re_im = kornia_detect(frame)
+    # elif method == "RetinaFace":
+    #     re_im = retina_detect(frame)
+    
+    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):
+    detector = face_detection.build_detector(
+    "RetinaNetResNet50", confidence_threshold=.5, nms_iou_threshold=.3)
+    
+    img_vis = base_detect(detector, img)
+    
+    return img_vis
+
+
+def retina_mobilenet_detect(img):
+    detector = face_detection.build_detector(
+    "RetinaNetMobileNetV1", confidence_threshold=.5, nms_iou_threshold=.3)
+    
+    img_vis = base_detect(detector, img)
+    
+    return img_vis
+
+
+def dsfd_detect(img):
+    detector = face_detection.build_detector(
+    "DSFDDetector", confidence_threshold=.5, nms_iou_threshold=.3)
+    
+    img_vis = base_detect(detector, img)
+    
+    return img_vis
+    
+
+
+def kornia_detect(img):
+    # select the device
+    device = torch.device('cpu')
+    vis_threshold = 0.6
+
+    # 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().to(device)
+
+    with torch.no_grad():
+        dets = face_detection(img)
+    dets = [FaceDetectorResult(o) for o in dets]
+
+    # show image
+
+    img_vis = img_raw.copy()
+
+    for b in dets:
+        if b.score < vis_threshold:
+            continue
+
+        # 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=0.9, value=0.5, label="Confidence Threshold")
+nms_slider = gr.components.Slider(minimum=0.1, maximum=0.9, value=0.5, label="Min Number of Neighbours")
+# scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
+# classifier_radio = gr.components.Radio(s)
+
+methods_dropdown = gr.components.Dropdown(["Kornia YuNet", "RetinaFace", "RetinaMobile", "DSFD"], value="Kornia YuNet", label="Choose a method")
+
+description = """Face Detection"""
+
+
+Iface = gr.Interface(
+    fn=detect_faces,
+    inputs=[input_image, methods_dropdown],#, size_slider, neighbour_slider, scale_slider],
+    outputs=[image_kornia, image_retina, image_retina_mobile, image_dsfd],
+    examples=[["data/9_Press_Conference_Press_Conference_9_86.jpg"], ["data/12_Group_Group_12_Group_Group_12_39.jpg"], ["data/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg"]],
+    title="Face Detection",
+).launch()

requirements.txt

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+kornia
+opencv-python
+git+https://github.com/hukkelas/DSFD-Pytorch-Inference.git