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import cv2 as cv
import numpy as np
import gradio as gr
from huggingface_hub import hf_hub_download
from pphumanseg import PPHumanSeg
# Download ONNX model from Hugging Face
model_path = hf_hub_download(repo_id="opencv/human_segmentation_pphumanseg", filename="human_segmentation_pphumanseg_2023mar.onnx")
# Initialize PPHumanSeg model
model = PPHumanSeg(
modelPath=model_path,
backendId=cv.dnn.DNN_BACKEND_OPENCV,
targetId=cv.dnn.DNN_TARGET_CPU
)
def get_color_map_list(num_classes):
num_classes += 1
color_map = num_classes * [0, 0, 0]
for i in range(num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
return color_map[3:]
def visualize(image, result, weight=0.6):
color_map = get_color_map_list(256)
color_map = np.array(color_map).reshape(256, 3).astype(np.uint8)
c1 = cv.LUT(result, color_map[:, 0])
c2 = cv.LUT(result, color_map[:, 1])
c3 = cv.LUT(result, color_map[:, 2])
pseudo_img = np.dstack((c1, c2, c3))
vis_result = cv.addWeighted(image, weight, pseudo_img, 1 - weight, 0)
return vis_result
def segment_person(input_image):
image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
h, w, _ = image.shape
resized = cv.resize(image, (192, 192))
resized = cv.cvtColor(resized, cv.COLOR_BGR2RGB)
result = model.infer(resized)
result = cv.resize(result[0, :, :], dsize=(w, h), interpolation=cv.INTER_NEAREST)
output = visualize(image, result)
output = cv.cvtColor(output, cv.COLOR_BGR2RGB)
return output
# Gradio Interface
demo = gr.Interface(
fn=segment_person,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=gr.Image(type="numpy", label="Human Segmentation Output"),
title="Human Segmentation PPHumanSeg (OpenCV DNN)",
allow_flagging="never",
description="Upload an image to segment human regions using OpenCV's ONNX-based PPHumanSeg model."
)
if __name__ == "__main__":
demo.launch()
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