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| import numpy as np | |
| import gradio as gr | |
| import math | |
| demo = gr.Blocks() | |
| def estimate(input_img): | |
| threshold = 0.9 | |
| img = Image.fromarray(input_img.astype('uint8'),'RGB') | |
| (w, h) = img.size | |
| image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]]) | |
| res = img2pose_model.predict([transform(img)])[0] | |
| all_bboxes = res["boxes"].cpu().numpy().astype('float') | |
| poses = [] | |
| bboxes = [] | |
| for i in range(len(all_bboxes)): | |
| if res["scores"][i] > threshold: | |
| bbox = all_bboxes[i] | |
| pose_pred = res["dofs"].cpu().numpy()[i].astype('float') | |
| pose_pred = pose_pred.squeeze() | |
| poses.append(pose_pred) | |
| bboxes.append(bbox) | |
| trans_vertices = renderer.transform_vertices(img, poses) | |
| img = renderer.render(img, trans_vertices, alpha=1) | |
| plt.figure(figsize=(8, 8)) | |
| for bbox in bboxes: | |
| plt.gca().add_patch(patches.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1],linewidth=3,edgecolor='b',facecolor='none')) | |
| plt.imshow(img) | |
| plt.show() | |
| pose_pred[0] = math.degrees(pose_pred[0]) | |
| pose_pred[1] = math.degrees(pose_pred[1]) | |
| pose_pred[2] = math.degrees(pose_pred[2]) | |
| listToStr = ' '.join([str(elem) for elem in pose_pred]) | |
| return img,pose_pred[0],pose_pred[1],pose_pred[2],pose_pred[3],pose_pred[4],pose_pred[5] | |
| def estimate_crowd(input_img): | |
| threshold = 0.5 | |
| img = Image.fromarray(input_img.astype('uint8'),'RGB') | |
| (w, h) = img.size | |
| image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]]) | |
| res = img2pose_model.predict([transform(img)])[0] | |
| all_bboxes = res["boxes"].cpu().numpy().astype('float') | |
| poses = [] | |
| bboxes = [] | |
| for i in range(len(all_bboxes)): | |
| if res["scores"][i] > threshold: | |
| bbox = all_bboxes[i] | |
| pose_pred = res["dofs"].cpu().numpy()[i].astype('float') | |
| pose_pred = pose_pred.squeeze() | |
| poses.append(pose_pred) | |
| bboxes.append(bbox) | |
| #render_plot(img.copy(), poses, bboxes) | |
| n = str((len(bboxes))) | |
| (w, h) = img.size | |
| image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]]) | |
| trans_vertices = renderer.transform_vertices(img, poses) | |
| img = renderer.render(img, trans_vertices, alpha=1) | |
| return img, n | |
| with demo: | |
| gr.Markdown("Head Pose Estimation in Crowd") | |
| with gr.Tabs(): | |
| with gr.TabItem("What is Head Pose Estimation?"): | |
| gr.Markdown( | |
| """ | |
| ### Head pose estimation in crowd (HPECS) is a task of estimating the head pose of people in the crowd scenes. | |
| Six degrees of freedom of head pose is the freedom of movement of the head pose in three-dimensional space. 6DoF is the | |
| abbreviation of six degrees of freedoms. 6DoF of head pose records the rotational motion such as | |
| pitch, yaw and roll , and translational motions of the head pose including up/down, left/right and front/back. | |
| 6DoF of 3D head pose is being regressed as it can harvest more versatile | |
| spatial information which able to track the movement of object even in a still image. | |
| """ | |
| ) | |
| gr.Image("/content/gdrive/MyDrive/project_folder/img2pose/6DoF.gif",size=20) | |
| with gr.TabItem("Head Pose Estimation"): | |
| gr.Markdown( | |
| """ | |
| ### Please insert image with single head | |
| """ | |
| ) | |
| image = gr.inputs.Image() | |
| text_button = gr.Button("Estimate") | |
| with gr.Row(): | |
| outputs=[gr.outputs.Image(),gr.outputs.Textbox(type="auto", label="Yaw"), | |
| gr.outputs.Textbox(type="auto", label="Pitch"), | |
| gr.outputs.Textbox(type="auto", label="Roll"), | |
| gr.outputs.Textbox(type="auto", label="tx"), | |
| gr.outputs.Textbox(type="auto", label="ty"), | |
| gr.outputs.Textbox(type="auto", label="tz")] | |
| with gr.TabItem("Estimate Head Pose in Crowd"): | |
| gr.Markdown( | |
| """ | |
| ### Please insert crowd scene image | |
| """ | |
| ) | |
| image_input = gr.Image() | |
| with gr.Row(): | |
| image_output = [gr.outputs.Image(),gr.outputs.Textbox(type="auto", label="Number of head pose estimated"), | |
| ] | |
| image_button = gr.Button("Estimate") | |
| text_button.click(estimate, inputs=image, outputs=outputs) | |
| image_button.click(estimate_crowd, inputs=image_input, outputs=image_output) | |
| demo.launch() |