app.py

import gradio as gr
import torch
from ultralytics import YOLO
import cv2
import numpy as np
from math import atan2, cos, sin, sqrt, pi

# Images
torch.hub.download_url_to_file('https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/cefd9731-c57c-428b-b401-fd54a8bd0a95', 'highway.jpg')
torch.hub.download_url_to_file('https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/acbad76a-33f9-4028-b012-4ece5998c272', 'highway1.jpg')
torch.hub.download_url_to_file('https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/7fa95f52-3c8b-4ea0-8bca-7374792a4c55', 'small-vehicles1.jpeg')

def drawAxis(img, p_, q_, color, scale):
  p = list(p_)
  q = list(q_)
 
  ## [visualization1]
  angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
  hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))
 
  # Here we lengthen the arrow by a factor of scale
  q[0] = p[0] - scale * hypotenuse * cos(angle)
  q[1] = p[1] - scale * hypotenuse * sin(angle)
  cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA)
 
  # create the arrow hooks
  p[0] = q[0] + 9 * cos(angle + pi / 4)
  p[1] = q[1] + 9 * sin(angle + pi / 4)
  cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA)
 
  p[0] = q[0] + 9 * cos(angle - pi / 4)
  p[1] = q[1] + 9 * sin(angle - pi / 4)
  cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA)
  ## [visualization1]
 

def getOrientation(pts, img):
  ## [pca]
  # Construct a buffer used by the pca analysis
  sz = len(pts)
  data_pts = np.empty((sz, 2), dtype=np.float64)
  for i in range(data_pts.shape[0]):
    data_pts[i,0] = pts[i,0,0]
    data_pts[i,1] = pts[i,0,1]
 
  # Perform PCA analysis
  mean = np.empty((0))
  mean, eigenvectors, eigenvalues = cv2.PCACompute2(data_pts, mean)
 
  # Store the center of the object
  cntr = (int(mean[0,0]), int(mean[0,1]))
  ## [pca]
 
  ## [visualization]
  # Draw the principal components
  cv2.circle(img, cntr, 3, (255, 0, 255), 10)
  p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0])
  p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0])
  drawAxis(img, cntr, p1, (255, 255, 0), 1)
  drawAxis(img, cntr, p2, (0, 0, 255), 3)
 
  angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians
  ## [visualization]
  angle_deg = -(int(np.rad2deg(angle))-180) % 180
 
  # Label with the rotation angle
  label = " Rotation Angle: " + str(int(np.rad2deg(angle))) + " degrees"
  textbox = cv2.rectangle(img, (cntr[0], cntr[1]-25), (cntr[0] + 250, cntr[1] + 10), (255,255,255), -1)
  cv2.putText(img, label, (cntr[0], cntr[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)

  return angle_deg

def yolov8_inference(
    image: gr.inputs.Image = None,
    model_path: gr.inputs.Dropdown = None,
    image_size: gr.inputs.Slider = 640,
    conf_threshold: gr.inputs.Slider = 0.25,
    iou_threshold: gr.inputs.Slider = 0.45,
):
    """
    YOLOv8 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """
    model = YOLO(model_path)
    model.conf = conf_threshold
    model.iou = iou_threshold
    #read image
    image = cv2.imread(image)
    #resize image (optional)
    img_res_toshow = cv2.resize(image, None, fx= 0.5, fy= 0.5, interpolation= cv2.INTER_LINEAR)
    height=img_res_toshow.shape[0]
    width=img_res_toshow.shape[1]
    dim=(width,height)
    results = model.predict(image, imgsz=image_size, return_outputs=True)
    #obtain BW image
    bw=(results[0].masks.masks[0].cpu().numpy() * 255).astype("uint8")
    #BW image with same dimention of initial image
    bw=cv2.resize(bw, dim, interpolation = cv2.INTER_AREA)
    img=img_res_toshow
    contours, _ = cv2.findContours(bw, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
    for i, c in enumerate(contours):
        # Calculate the area of each contour
        area = cv2.contourArea(c)
        
        # Ignore contours that are too small or too large
        if area < 3700 or 100000 < area:
            continue
        
        # Draw each contour only for visualisation purposes
        cv2.drawContours(img, contours, i, (0, 0, 255), 2)
        
        # Find the orientation of each shape
        print(getOrientation(c, img))
        
    return img

inputs = [
    gr.inputs.Image(type="filepath", label="Input Image"),
    gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"], 
                       default="kadirnar/yolov8m-v8.0", label="Model"),
    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"

examples = [['highway.jpg', 'kadirnar/yolov8m-v8.0', 640, 0.25, 0.45], ['highway1.jpg', 'kadirnar/yolov8l-v8.0', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'kadirnar/yolov8x-v8.0', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
    fn=yolov8_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    examples=examples,
    cache_examples=True,
    theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)