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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 | |
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_dir = 'saved_model' | |
detection_model = tf.saved_model.load(str(model_dir)) | |
return detection_model | |
def predict(image_np): | |
image_np = pil_image_as_numpy_array(image_np) | |
image_np = np.expand_dims(image_np, axis=0) | |
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 | |
detection_model = load_model() | |
# Specify paths to example images | |
sample_images = [["test_1.jpg"],["test_9.jpg"],["test_6.jpg"],["test_7.jpg"], | |
["test_10.jpg"], ["test_11.jpg"],["test_8.jpg"]] | |
tab1 = gr.Interface(fn=predict, | |
inputs=gr.Image(label='Upload an expressway image', type="pil"), | |
outputs=gr.Image(type="pil"), | |
title='Blue and Yellow Taxi detection in live expressway traffic conditions (data.gov.sg)', | |
examples = sample_images | |
) | |
def predict_on_video(video_in_filepath, video_out_filepath, detection_model, category_index): | |
video_reader = cv2.VideoCapture(video_in_filepath) | |
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) | |
) | |
label_id_offset = 0 | |
while True: | |
ret, frame = video_reader.read() | |
if not ret: | |
break # Break the loop if the video is finished | |
processed_frame = predict(frame, detection_model, category_index, label_id_offset) | |
# Convert processed frame to numpy array | |
processed_frame_np = np.array(processed_frame) | |
# Write the frame to the output video | |
video_writer.write(processed_frame_np) | |
# Release video reader and writer | |
video_reader.release() | |
video_writer.release() | |
cv2.destroyAllWindows() | |
cv2.waitKey(1) | |
# Function to process a video | |
def process_video(video_path): | |
output_path = "output_video.mp4" # Output path for the processed video | |
# Assuming you have detection_model and category_index defined | |
predict_on_video(video_path, output_path, detection_model, category_index) | |
return output_path | |
# Create the video processing interface | |
tab2 = gr.Interface( | |
fn=process_video, | |
inputs=gr.File(label="Upload a video", type="video", accept=".mp4"), | |
outputs=gr.File(type="video/mp4"), | |
title='Video Processing', | |
examples=["example_video.mp4"] | |
) | |
# Create a Tabbed Interface | |
iface = gr.Tabbed(tab1, tab2) | |
# Launch the interface | |
iface.launch(share=True) |