import matplotlib.pyplot as plt import numpy as np from six import BytesIO from PIL import Image import tensorflow as tf from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as viz_utils from object_detection.utils import ops as utils_op import tarfile import wget import gradio as gr from huggingface_hub import snapshot_download import os import matplotlib.pyplot as plt from tqdm import tqdm import cv2 PATH_TO_LABELS = 'data/label_map.pbtxt' category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True) def pil_image_as_numpy_array(pilimg): img_array = tf.keras.utils.img_to_array(pilimg) img_array = np.expand_dims(img_array, axis=0) return img_array def load_image_into_numpy_array(path): image = None image_data = tf.io.gfile.GFile(path, 'rb').read() image = Image.open(BytesIO(image_data)) return pil_image_as_numpy_array(image) def load_model(): download_dir = snapshot_download(REPO_ID) saved_model_dir = os.path.join(download_dir, "saved_model") detection_model = tf.saved_model.load(saved_model_dir) return detection_model def load_model2(): wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz") tarfile.open("balloon_model.tar.gz").extractall() model_dir = 'saved_model' detection_model = tf.saved_model.load(str(model_dir)) return detection_model threshold = 0.50 def predict(pilimg,video_in_filepath,threshold): if pilimg != "": image_np = pil_image_as_numpy_array(pilimg) return predict2(image_np,threshold),None else: video_reader = cv2.VideoCapture(video_in_filepath) nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT)) frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH)) fps = video_reader.get(cv2.CAP_PROP_FPS) video_out_filepath = 'detected.mp4' video_writer = cv2.VideoWriter(video_out_filepath, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h)) for i in tqdm(range(nb_frames)): ret, image_np = video_reader.read() input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8) results = detection_model(input_tensor) viz_utils.visualize_boxes_and_labels_on_image_array( image_np, results['detection_boxes'][0].numpy(), (results['detection_classes'][0].numpy()+ label_id_offset).astype(int), results['detection_scores'][0].numpy(), category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, min_score_thresh=.50, agnostic_mode=False, line_thickness=2) video_writer.write(np.uint8(image_np)) # Release camera and close windows video_reader.release() video_writer.release() cv2.destroyAllWindows() cv2.waitKey(1) return None,video_out_filepath def predict2(image_np,threshold): results = detection_model(image_np) # different object detection models have additional results result = {key:value.numpy() for key,value in results.items()} label_id_offset = 0 image_np_with_detections = image_np.copy() viz_utils.visualize_boxes_and_labels_on_image_array( image_np_with_detections[0], result['detection_boxes'][0], (result['detection_classes'][0] + label_id_offset).astype(int), result['detection_scores'][0], category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, min_score_thresh=float(threshold), agnostic_mode=False, line_thickness=2) result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) return result_pil_img label_id_offset = 0 samples_folder = 'test_samples' # image_path = 'test_samples/image489.png' def video_fn(video_in_filepath): video_reader = cv2.VideoCapture(video_in_filepath) nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT)) frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH)) fps = video_reader.get(cv2.CAP_PROP_FPS) video_out_filepath = 'detected.mp4' video_writer = cv2.VideoWriter(video_out_filepath, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h)) for i in tqdm(range(nb_frames)): ret, image_np = video_reader.read() input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8) results = detection_model(input_tensor) viz_utils.visualize_boxes_and_labels_on_image_array( image_np, results['detection_boxes'][0].numpy(), (results['detection_classes'][0].numpy()+ label_id_offset).astype(int), results['detection_scores'][0].numpy(), category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, min_score_thresh=.50, agnostic_mode=False, line_thickness=2) video_writer.write(np.uint8(image_np)) # Release camera and close windows video_reader.release() video_writer.release() cv2.destroyAllWindows() cv2.waitKey(1) return video_out_filepath REPO_ID = "23b719w/assignment2_tfodmodel" detection_model = load_model() # pil_image = Image.open(image_path) # image_arr = pil_image_as_numpy_array(pil_image) # predicted_img = predict(image_arr) # predicted_img.save('predicted.jpg') gr.Interface(fn=predict, inputs=[gr.Image(type="pil",label="Input Image"),gr.Video(label="Input Video"),gr.Textbox(placeholder="0.50",label="Set the confidence threshold (0.00-1.00)")], outputs=[gr.Image(type="pil",label="Output Image"),gr.Video(label="Output Video")], title="Facemask & Glasses", description="Model: ssd_mobilenet_v2_320x320", theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), examples=[["test_samples/image489.png","",0.55], ["test_samples/image825.png","",0.55], ["test_samples/image833.png","",0.55], ["test_samples/image846.png","",0.55]] ).launch(share=True) # gr.Interface(fn=video_fn, # inputs=gr.Video(label="Input Video"), # outputs=gr.Video(label="Output Video"), # title="Facemask & Glasses", # description="Model: ssd_mobilenet_v2_320x320", # theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), # #examples="test_samples/test_video.mp4" # ).launch(share=True)