Spaces:
Running
Running
File size: 3,990 Bytes
b03e0d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import os
import cv2
import cv2 as cv
import numpy as np
import gradio as gr
from yunet import YuNet
# Valid combinations of backends and targets
backend_target_pairs = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16],
[cv.dnn.DNN_BACKEND_TIMVX, cv.dnn.DNN_TARGET_NPU],
[cv.dnn.DNN_BACKEND_CANN, cv.dnn.DNN_TARGET_NPU],
]
class ImageResizer:
def __init__(
self,
modelPath,
input_size=(320, 320),
conf_threshold=0.6,
nms_threshold=0.3,
top_k=5000,
backend_id=0,
target_id=0,
):
self.model = YuNet(
modelPath=modelPath,
inputSize=input_size,
confThreshold=conf_threshold,
nmsThreshold=nms_threshold,
topK=top_k,
backendId=backend_id,
targetId=target_id,
)
def detect(self, image, num_faces=None):
# If input is an image
if image is not None:
h, w, _ = image.shape
# Inference
self.model.setInputSize([w, h])
results = self.model.infer(image)
faces = results[:num_faces] if num_faces else results
bboxs = []
for face in faces:
bbox = face[0:4].astype(np.int32) # x,y,w,h
x, y, w, h = bbox
# draw
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
bboxs.append(bbox)
return image, bboxs
def resize(self, image, target_size=512, above_head_ratio=0.5):
height, width, _c = image.shape
ar = width / height
# downscale the image
if not target_size:
target_size = 512
if ar > 1:
# Landscape
new_height = target_size
new_width = int(target_size * ar)
elif ar < 1:
# Portrait
new_width = target_size
new_height = int(target_size / ar)
else:
# Square
new_width = target_size
new_height = target_size
resized = cv2.resize(
image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
)
# Perform object detection on the resized image
dt_image, bboxes = self.detect(resized.copy())
# crop around face
if len(bboxes) >= 1:
x, y, w, h = bboxes[0]
else:
x, y, w, h = 0, 0, target_size, target_size
# 20% of image height
above_head_max = int(target_size * above_head_ratio)
x_center = int((x + (x + w)) / 2)
y_center = int((y + (y + h)) / 2)
# Calculate cropping box
left = int(max(0, x_center - target_size // 2))
top = int(max(0, y_center - above_head_max))
right = min(left + target_size, resized.shape[1])
bottom = min(top + target_size, resized.shape[0])
cropped_image = resized[top:bottom, left:right]
return dt_image, cropped_image
model_path = "face_detection_yunet_2023mar.onnx"
image_resizer = ImageResizer(modelPath=model_path)
def face_detector(input_image, target_size=512):
return image_resizer.resize(input_image, target_size)
inputs = [
gr.Image(sources=["upload", "clipboard"], type="numpy"),
gr.Dropdown(
choices=[512, 768, 1024],
value=512,
allow_custom_value=True,
info="Target size of images",
),
]
outputs = [
gr.Image(label="face detection", format="JPEG"),
gr.Image(label="focused resized", format="JPEG"),
]
demo = gr.Interface(
fn=face_detector,
inputs=inputs,
outputs=outputs,
title="Image Resizer",
theme="gradio/monochrome",
api_name="resize",
submit_btn=gr.Button("Resize", variant="primary"),
allow_flagging="never",
)
demo.queue(
max_size=10,
)
if __name__ == "__main__":
demo.launch()
|