app.py

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
from tqdm import tqdm
import tarfile
import wget 
import gradio as gr
from huggingface_hub import snapshot_download
import os 
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)
label_id_offset = 0

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    

# samples_folder = 'test_samples
# image_path = 'test_samples/sample_balloon.jpeg
# 

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_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
    
    return result_pil_img
def write_video(video_in_filepath, video_out_filepath, detection_model):
       
    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_writer = cv2.VideoWriter(video_out_filepath,
                               cv2.VideoWriter_fourcc(*'mp4v'), 
                               fps, 
                               (frame_w, frame_h))

    while True:
        ret, image_np = video_reader.read()
        if not ret:
            break

        results = predict(image_np)
        results_np = np.array(results)
        video_writer.write(results_np)
        #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)
    
def predict_video (video_file_name):
    detected_video_file = "detected_video.mp4"
    write_video(video_file_name,detected_video_file,detection_model)
    return detected_video_file

REPO_ID = "YEHTUT/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')
Image_tab = gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs=gr.Image(type="pil"),
             examples=[["SampleImage1.jpg"],["SampleImage2.jpg"],["SampleImage3.jpg"],["SampleImage4.jpg"],["SampleImage5.jpg"],["SampleImage6.jpg"]],
            title="This is the object detection model for Durian and Pineapple images",
            description="Using ssd_mobilenet_v2_320x320_coco17_tpu-8 to detect Durina and Pineapple"
             )
Video_tab = gr.Interface(fn=predict_video,
             inputs=gr.Video(label="Upload Video"),
             outputs=gr.Video(label="Detected Video"),
             examples=[["SampleVideo1.mp4"]],
            title="This is the object detection model for Durian and Pineapple videos",
            description="Using ssd_mobilenet_v2_320x320_coco17_tpu-8 to detect Durina and Pineapple"                         
             )

gr.TabbedInterface([Image_tab, Video_tab], ["Image", "Video"]).launch(share=True)
#gr.Interface(fn=predict,
#             inputs=gr.Image(type="pil"),
#             outputs=gr.Image(type="pil")
#             ).launch(share=True)