import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
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
    results = model.predict(image, imgsz=image_size, return_outputs=True)
    object_prediction_list = []
    for _, image_results in enumerate(results):
        if len(image_results)!=0:
            image_predictions_in_xyxy_format = image_results['det']
            for pred in image_predictions_in_xyxy_format:
                x1, y1, x2, y2 = (
                    int(pred[0]),
                    int(pred[1]),
                    int(pred[2]),
                    int(pred[3]),
                )
                bbox = [x1, y1, x2, y2]
                score = pred[4]
                category_name = model.model.names[int(pred[5])]
                category_id = pred[5]
                object_prediction = ObjectPrediction(
                    bbox=bbox,
                    category_id=int(category_id),
                    score=score,
                    category_name=category_name,
                )
                object_prediction_list.append(object_prediction)

    image = read_image(image)
    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
    return output_image['image']

inputs = [
    gr.inputs.Image(type="filepath", label="Input Image"),
    gr.inputs.Dropdown(["turhancan97/yolov8m-trash"], 
                       default="turhancan97/yolov8m-trash", 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', 'turhancan97/yolov8m-trash', 640, 0.25, 0.45], ['highway1.jpg', 'turhancan97/yolov8m-trash', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'turhancan97/yolov8m-trash', 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)