| import cv2 as cv |
| import numpy as np |
| import gradio as gr |
| from huggingface_hub import hf_hub_download |
| from pphumanseg import PPHumanSeg |
|
|
| |
| model_path = hf_hub_download(repo_id="opencv/human_segmentation_pphumanseg", filename="human_segmentation_pphumanseg_2023mar.onnx") |
|
|
| |
| 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 |
|
|
| |
| with gr.Blocks(css='''.example * { |
| font-style: italic; |
| font-size: 18px !important; |
| color: #0ea5e9 !important; |
| }''') as demo: |
|
|
| gr.Markdown("### Human Segmentation PPHumanSeg (OpenCV DNN)") |
| gr.Markdown("Upload an image to segment human regions using OpenCV's ONNX-based PPHumanSeg model.") |
|
|
| with gr.Row(): |
| input_image = gr.Image(type="numpy", label="Upload Image") |
| output_image = gr.Image(type="numpy", label="Human Segmentation Output") |
|
|
| |
| input_image.change(fn=lambda: (None), outputs=output_image) |
|
|
| with gr.Row(): |
| submit_btn = gr.Button("Submit", variant="primary") |
| clear_btn = gr.Button("Clear") |
|
|
| submit_btn.click(fn=segment_person, inputs=input_image, outputs=output_image) |
| clear_btn.click(fn=lambda:(None, None), outputs=[input_image, output_image]) |
|
|
| gr.Markdown("Click on any example to try it.", elem_classes=["example"]) |
|
|
| gr.Examples( |
| examples=[ |
| ["examples/googlenet_0.png"], |
| ["examples/gray_face.png"], |
| ["examples/messi5.jpg"] |
| ], |
| inputs=input_image |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
