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import cv2
import numpy as np
import gradio as gr
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 Detection",
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)
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)
yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
else:
cap = cv2.VideoCapture(vid)
model, classes, colors, output_layers = load_yolo()
while(cap.isOpened()):
ret1,frame1 = cap.read()
if ret1:
height, width, channels = frame1.shape
blob, outputs = detect_objects_yolo(frame1, model, output_layers)
boxes, confs, class_ids = get_box_dimensions_yolo(outputs, height, width)
frame=draw_labels_yolo(boxes, confs, colors, class_ids, classes, frame1)
yield cv2.cvtColor(frame1, cv2.COLOR_BGR2RGB)
model_name = gr.Radio(["MobileNet-SSD", "YOLOv3"], value="YOLOv3", label="Model", info="choose your model")
inputs_video = gr.Video(sources=None, label="Input Video")
outputs_video = [
gr.Image(type="numpy", label="Output Video"),
]
interface_video = gr.Interface(
fn=vid_inf,
inputs=[inputs_video,model_name],
outputs=outputs_video,
title="Video Detection",
description="upload your video and select one model and see the results!",
examples=[["sample/video_1.mp4"]],
cache_examples=False,
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference'],
title='Object Detection(MobileNet-SSDxYOLOv3)'
).queue().launch() |