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| import gdown | |
| import gradio as gr | |
| import logging | |
| import os | |
| import cv2 | |
| import numpy as np | |
| import tensorflow as tf | |
| from ai.detection import detect | |
| from laeo_per_frame.interaction_per_frame_uncertainty import LAEO_computation | |
| from utils.hpe import hpe, project_ypr_in2d | |
| from utils.img_util import resize_preserving_ar, draw_detections, percentage_to_pixel, draw_key_points_pose, \ | |
| visualize_vector | |
| def load_image(camera, ): | |
| # Capture the video frame by frame | |
| try: | |
| ret, frame = camera.read() | |
| return True, frame | |
| except: | |
| logging.Logger('Error reading frame') | |
| return False, None | |
| def demo_play(img, laeo=True, rgb=False): | |
| # webcam in use | |
| # gpus = tf.config.list_physical_devices('GPU') | |
| # img = np.array(frame) | |
| if not rgb: | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) | |
| img_resized, new_old_shape = resize_preserving_ar(img, input_shape_od_model) | |
| print('inference centernet') | |
| detections, elapsed_time = detect(model, img_resized, min_score_thresh, | |
| new_old_shape) # detection classes boxes scores | |
| # probably to draw on resized | |
| img_with_detections = draw_detections(img_resized, detections, max_boxes_to_draw, None, None, None) | |
| # cv2.imshow("aa", img_with_detections) | |
| det, kpt = percentage_to_pixel(img.shape, detections['detection_boxes'], detections['detection_scores'], | |
| detections['detection_keypoints'], detections['detection_keypoint_scores']) | |
| # center_xy, yaw, pitch, roll = head_pose_estimation(kpt, 'centernet', gaze_model=gaze_model) | |
| # _________ extract hpe and print to img | |
| people_list = [] | |
| print('inferece hpe') | |
| for j, kpt_person in enumerate(kpt): | |
| yaw, pitch, roll, tdx, tdy = hpe(gaze_model, kpt_person, detector='centernet') | |
| # img = draw_axis_3d(yaw[0].numpy()[0], pitch[0].numpy()[0], roll[0].numpy()[0], image=img, tdx=tdx, tdy=tdy, | |
| # size=50) | |
| people_list.append({'yaw' : yaw[0].numpy()[0], | |
| 'yaw_u' : 0, | |
| 'pitch' : pitch[0].numpy()[0], | |
| 'pitch_u' : 0, | |
| 'roll' : roll[0].numpy()[0], | |
| 'roll_u' : 0, | |
| 'center_xy': [tdx, tdy] | |
| }) | |
| for i in range(len(det)): | |
| img = draw_key_points_pose(img, kpt[i]) | |
| # call LAEO | |
| clip_uncertainty = 0.5 | |
| binarize_uncertainty = False | |
| if laeo: | |
| interaction_matrix = LAEO_computation(people_list, clipping_value=clip_uncertainty, | |
| clip=binarize_uncertainty) | |
| else: | |
| interaction_matrix = np.zeros((len(people_list), len(people_list))) | |
| # coloured arrow print per person | |
| for index, person in enumerate(people_list): | |
| green = round((max(interaction_matrix[index, :])) * 255) | |
| colour = (0, green, 0) | |
| if green < 40: | |
| colour = (0, 0, 255) | |
| vector = project_ypr_in2d(person['yaw'], person['pitch'], person['roll']) | |
| img = visualize_vector(img, person['center_xy'], vector, title="", | |
| color=colour) | |
| return img | |
| demo_webcam = gr.Interface( | |
| fn=demo_play, | |
| inputs=[gr.Image(source="webcam"), # with no streaming-> acquire images | |
| gr.Checkbox(value=True, label="LAEO", info="Compute and display LAEO"), | |
| gr.Checkbox(value=False, label="rgb", info="Display output on W/B image"), | |
| ], | |
| outputs="image", | |
| live=True, | |
| title="Head Pose Estimation and LAEO", | |
| description="This is a demo developed by Federico Figari T. at MaLGa Lab, University of Genoa, Italy. You can choose to have only the Head Pose Estimation or also the LAEO computation (more than 1 person should be in the image). You need to take a picture and the algorithm will calculate the Head Pose and will be showed as an arrow on your face. LAEO, instead is showed colouring the arrow in green.", | |
| # examples=EXAMPLES, | |
| ) | |
| demo_upload = gr.Interface( | |
| fn=demo_play, | |
| inputs=[gr.Image(source="upload",), # with no streaming-> acquire images | |
| gr.Checkbox(value=True, label="LAEO", info="Compute and display LAEO"), | |
| gr.Checkbox(value=False, label="rgb", info="Display output on W/B image"), | |
| ], | |
| outputs="image", | |
| live=True, | |
| title="Head Pose Estimation and LAEO", | |
| description="This is a demo developed by Federico Figari T. at MaLGa Lab, University of Genoa, Italy. You can choose to have only the Head Pose Estimation or also the LAEO computation (more than 1 person should be in the image). You need to upload an image and the algorithm will calculate the Head Pose and will be showed as an arrow on your face. LAEO, instead is showed colouring the arrow in green.", | |
| examples=[["LAEO_demo_data/examples/1.jpg"], ["LAEO_demo_data/examples/20.jpg"]] | |
| ) | |
| demo_tabbed = gr.TabbedInterface([demo_webcam, demo_upload], ["Demo from webcam", "Demo from upload"]) | |
| if __name__=='__main__': | |
| if not os.path.exists("LAEO_demo_data"): | |
| gdown.download_folder("https://drive.google.com/drive/folders/1nQ1Cb_tBEhWxy183t-mIcVH7AhAfa6NO?usp=drive_link", | |
| use_cookies=False) | |
| # Get the list of all files and directories | |
| path = "LAEO_demo_data/examples" | |
| dir_list = os.listdir(path) | |
| print("Files and directories in '", path, "' :") | |
| # prints all files | |
| print(dir_list) | |
| gaze_model_path = 'LAEO_demo_data/head_pose_estimation' | |
| gaze_model = tf.keras.models.load_model(gaze_model_path, custom_objects={"tf": tf}) | |
| path_to_model = 'LAEO_demo_data/keypoint_detector/centernet_hg104_512x512_kpts_coco17_tpu-32' | |
| model = tf.saved_model.load(os.path.join(path_to_model, 'saved_model')) | |
| input_shape_od_model = (512, 512) | |
| # params | |
| min_score_thresh, max_boxes_to_draw, min_distance = .45, 50, 1.5 | |
| print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) | |
| demo_tabbed.launch() |