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 cv2 from tqdm import tqdm 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(model_repo_id): download_dir = snapshot_download(model_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 predict(pilimg): image_np = pil_image_as_numpy_array(pilimg) return predict2(image_np) def predict2(image_np): 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=.60, agnostic_mode=False, line_thickness=2) result_pil_img2 = tf.keras.utils.array_to_img(image_np_with_detections[0]) return result_pil_img2 def predict3(pilimg): image_np = pil_image_as_numpy_array(pilimg) return predict4(image_np) def predict4(image_np): results = detection_model2(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=.60, agnostic_mode=False, line_thickness=2) result_pil_img4 = tf.keras.utils.array_to_img(image_np_with_detections[0]) return result_pil_img4 def detect_video(video): # Create a video capture object cap = cv2.VideoCapture(video) # Process frames in a loop while cap.isOpened(): ret, frame = cap.read() if not ret: break # Expand dimensions since model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(frame, axis=0) # Run inference output_dict = model(image_np_expanded) # Extract detections boxes = output_dict['detection_boxes'][0].numpy() scores = output_dict['detection_scores'][0].numpy() classes = output_dict['detection_classes'][0].numpy().astype(np.int64) # Draw bounding boxes and labels image_np_with_detections = viz_utils.visualize_boxes_and_labels_on_image_array( frame, boxes, classes, scores, category_index, use_normalized_coordinates=True, max_boxes_to_draw=20, min_score_thresh=.5, agnostic_mode=False) # Yield the processed frame yield image_np_with_detections # Release resources cap.release() label_id_offset = 0 REPO_ID = "apailang/mytfodmodel" detection_model = load_model(REPO_ID) REPO_ID2 = "apailang/mytfodmodeltuned" detection_model2 = load_model(REPO_ID2) samples_folder = 'data' # 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') test1 = os.path.join(os.path.dirname(__file__), "data/test1.jpeg") test2 = os.path.join(os.path.dirname(__file__), "data/test2.jpeg") test3 = os.path.join(os.path.dirname(__file__), "data/test3.jpeg") test4 = os.path.join(os.path.dirname(__file__), "data/test4.jpeg") test5 = os.path.join(os.path.dirname(__file__), "data/test5.jpeg") test6 = os.path.join(os.path.dirname(__file__), "data/test6.jpeg") test7 = os.path.join(os.path.dirname(__file__), "data/test7.jpeg") test8 = os.path.join(os.path.dirname(__file__), "data/test8.jpeg") test9 = os.path.join(os.path.dirname(__file__), "data/test9.jpeg") test10 = os.path.join(os.path.dirname(__file__), "data/test10.jpeg") test11 = os.path.join(os.path.dirname(__file__), "data/test11.jpeg") test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg") base_image = gr.Interface( fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Image(type="pil"), title="Luffy and Chopper face detection (Base mobile net model)", description="Upload a Image for prediction or click on below examples", examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],], cache_examples=True )#.launch(share=True) tuned_image = gr.Interface( fn=predict3, inputs=gr.Image(type="pil"), outputs=gr.Image(type="pil"), title="Luffy and Chopper face detection (tuned mobile net model)", description="Upload a Image for prediction or click on below examples. Mobile net tuned with data Augmentation", examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],], cache_examples=True )#.launch(share=True) a = os.path.join(os.path.dirname(__file__), "data/a.mp4") # Video b = os.path.join(os.path.dirname(__file__), "data/b.mp4") # Video c = os.path.join(os.path.dirname(__file__), "data/c.mp4") # Video video_out_file = os.path.join(samples_folder,'detected' + '.mp4') stt_demo = gr.Interface( fn=detect_video, inputs=gr.Video(), outputs=gr.Video(label="Detected Video"), examples=[ [a], [b], [c], ], cache_examples=False ) demo = gr.TabbedInterface([base_image,tuned_image, stt_demo], ["Image (base model)","Image (tuned model)", "Video"]) if __name__ == "__main__": demo.launch()