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import cv2
import numpy as np
import gradio as gr # type: ignore
from mbnet import load_model, detect_objects, get_box_dimensions, draw_labels, load_img
from yolov3 import load_image, load_yolo, detect_objects_yolo, get_box_dimensions_yolo, draw_labels_yolo
# Image Inference
def img_inf(img,model):
if model=="MobileNet-SSD":
model, classes, colors = load_model()
image, height, width, channels = load_img(img)
blob, outputs = detect_objects(image, model)
boxes, class_ids = get_box_dimensions(outputs, height, width)
image1 = draw_labels(boxes, colors, class_ids, classes, image)
return cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
else:
model, classes, colors, output_layers = load_yolo()
image, height, width, channels = load_image(img)
blob, outputs = detect_objects_yolo(image, model, output_layers)
boxes, confs, class_ids = get_box_dimensions_yolo(outputs, height, width)
image=draw_labels_yolo(boxes, confs, colors, class_ids, classes, image)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
model_name = gr.Radio(["MobileNet-SSD", "YOLOv3"], value="YOLOv3", label="Model", info="choose your model")
inputs_image = gr.Image(type="filepath", label="Input Image")
outputs_image = gr.Image(type="numpy", label="Output Image")
interface_image = gr.Interface(
fn=img_inf,
inputs=[inputs_image,model_name],
outputs=outputs_image,
title="Image Inference",
description="Upload your photo and select one model and see the results!",
examples=[["sample/dog.jpg"]],
cache_examples=False,
)
# Video Inference
def vid_inf(vid, model_type):
if model_type == "MobileNet-SSD":
cap = cv2.VideoCapture(vid)
# get the video frames' width and height for proper saving of videos
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_size = (frame_width, frame_height)
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
output_video = "output_recorded.mp4"
# create the `VideoWriter()` object
out = cv2.VideoWriter(output_video, fourcc, fps, frame_size)
model, classes, colors = load_model()
while cap.isOpened():
ret, frame = cap.read()
if ret:
height, width, channels = frame.shape
blob, outputs = detect_objects(frame, model)
boxes, class_ids = get_box_dimensions(outputs, height, width)
frame = draw_labels(boxes, colors, class_ids, classes, frame)
out.write(frame)
yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),None
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
yield None, output_video
else:
cap = cv2.VideoCapture(vid)
# get the video frames' width and height for proper saving of videos
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_size = (frame_width, frame_height)
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
output_video = "output_recorded.mp4"
# create the `VideoWriter()` object
out = cv2.VideoWriter(output_video, fourcc, fps, frame_size)
model, classes, colors, output_layers = load_yolo()
while cap.isOpened():
ret, frame_y = cap.read()
if ret:
height, width, channels = frame_y.shape
blob, outputs = detect_objects_yolo(frame_y, model, output_layers)
boxes, confs, class_ids = get_box_dimensions_yolo(outputs, height, width)
frame_y = draw_labels_yolo(boxes, confs, colors, class_ids, classes, frame_y)
out.write(frame_y)
yield cv2.cvtColor(frame_y, cv2.COLOR_BGR2RGB), None
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
yield None, output_video
model_name = gr.Radio(["MobileNet-SSD", "YOLOv3"], value="YOLOv3", label="Model", info="choose your model")
input_video = gr.Video(sources=None, label="Input Video")
output_frame = gr.Image(type="numpy", label="Output Frames")
output_video_file = gr.Video(label="Output video")
interface_video = gr.Interface(
fn=vid_inf,
inputs=[input_video, model_name],
outputs=[output_frame,output_video_file],
title="Video Inference",
description="Upload your video and select one model and see the results!",
examples=[["sample/video_1.mp4"],["sample/person.mp4"]],
cache_examples=False,
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image', 'Video'],
title='GradioxOpenCV-DNN'
).queue().launch() |