import cv2
import numpy as np
import json
import gradio as gr
import os
import xml.etree.ElementTree as ET

def get_rotated_rect_corners(x, y, w, h, rotation_deg):
    rot_rad = np.deg2rad(rotation_deg)
    cos_r, sin_r = np.cos(rot_rad), np.sin(rot_rad)
    R = np.array([[cos_r, -sin_r], [sin_r, cos_r]])
    cx, cy = x + w/2, y + h/2
    local_corners = np.array([[-w/2,-h/2],[w/2,-h/2],[w/2,h/2],[-w/2,h/2]])
    rotated_corners = np.dot(local_corners, R.T)
    return (rotated_corners + np.array([cx,cy])).astype(np.float32)

def preprocess_gray_clahe(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    return clahe.apply(gray)

def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
    if method=="SIFT": detector=cv2.SIFT_create(nfeatures=5000); matcher=cv2.BFMatcher(cv2.NORM_L2)
    elif method=="ORB": detector=cv2.ORB_create(5000); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
    elif method=="BRISK": detector=cv2.BRISK_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
    elif method=="KAZE": detector=cv2.KAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_L2)
    elif method=="AKAZE": detector=cv2.AKAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
    else: return None,None,[]
    
    kp1, des1 = detector.detectAndCompute(img1_gray,None)
    kp2, des2 = detector.detectAndCompute(img2_gray,None)
    if des1 is None or des2 is None: return None,None,[]
    
    raw_matches = matcher.knnMatch(des1,des2,k=2)
    good = [m for m,n in raw_matches if m.distance < ratio_thresh*n.distance]
    return kp1, kp2, good

def parse_xml_points(xml_file):
    tree = ET.parse(xml_file)
    root = tree.getroot()
    points=[]
    for pt_type in ["TopLeft","TopRight","BottomLeft","BottomRight"]:
        elem=root.find(f".//point[@type='{pt_type}']")
        points.append([float(elem.get("x")), float(elem.get("y"))])
    return np.array(points,dtype=np.float32).reshape(-1,2)

def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
    flat_img = cv2.imread(flat_file)
    persp_img = cv2.imread(persp_file)
    
    # FIX: Use the file paths directly instead of .name
    mockup = json.load(open(json_file))
    roi_data = mockup["printAreas"][0]["position"]
    roi_x, roi_y = roi_data["x"], roi_data["y"]
    roi_w, roi_h = mockup["printAreas"][0]["width"], mockup["printAreas"][0]["height"]
    roi_rot_deg = mockup["printAreas"][0]["rotation"]

    flat_gray = preprocess_gray_clahe(flat_img)
    persp_gray = preprocess_gray_clahe(persp_img)
    
    # FIX: Use the file path directly instead of .name
    xml_points = parse_xml_points(xml_file)

    methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
    gallery_files = []
    download_files = []

    for method in methods:
        kp1,kp2,good_matches = detect_and_match(flat_gray,persp_gray,method)
        if kp1 is None or kp2 is None or len(good_matches)<4: continue

        match_img = cv2.drawMatches(flat_img,kp1,persp_img,kp2,good_matches,None,flags=2)

        src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1,1,2)
        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1,1,2)
        H,_ = cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,5.0)
        if H is None: continue

        roi_corners_flat = get_rotated_rect_corners(roi_x,roi_y,roi_w,roi_h,roi_rot_deg)
        roi_corners_persp = cv2.perspectiveTransform(roi_corners_flat.reshape(-1,1,2),H).reshape(-1,2)
        persp_roi = persp_img.copy()
        cv2.polylines(persp_roi,[roi_corners_persp.astype(int)],True,(0,255,0),2)
        for px,py in roi_corners_persp: cv2.circle(persp_roi,(int(px),int(py)),5,(255,0,0),-1)

        xml_gt_img = persp_img.copy()
        xml_mapped = cv2.perspectiveTransform(xml_points.reshape(-1,1,2),H).reshape(-1,2)
        for px,py in xml_mapped: cv2.circle(xml_gt_img,(int(px),int(py)),5,(0,0,255),-1)

        # Merge 2x2 grid (original resolution)
        top = np.hstack([flat_img, match_img])
        bottom = np.hstack([persp_roi, xml_gt_img])
        combined_grid = np.vstack([top, bottom])

        # Save combined grid as file
        base_name = os.path.splitext(os.path.basename(persp_file))[0]
        file_name = f"{base_name}_{method.lower()}.png"
        cv2.imwrite(file_name, combined_grid)
        gallery_files.append(file_name)
        download_files.append(file_name)

    # Ensure 5 download slots
    while len(download_files)<5: download_files.append(None)
    return gallery_files, download_files[0], download_files[1], download_files[2], download_files[3], download_files[4]

iface = gr.Interface(
    fn=homography_all_detectors,
    inputs=[
        gr.Image(label="Upload Flat Image", type="filepath"),
        gr.Image(label="Upload Perspective Image", type="filepath"),
        gr.File(label="Upload mockup.json", file_types=[".json"]),
        gr.File(label="Upload XML file", file_types=[".xml"])
    ],
    outputs=[
        gr.Gallery(label="Results per Detector", show_label=True),
        gr.File(label="Download SIFT Result"),
        gr.File(label="Download ORB Result"),
        gr.File(label="Download BRISK Result"),
        gr.File(label="Download KAZE Result"),
        gr.File(label="Download AKAZE Result")
    ],
    title="Homography ROI Projection with Feature Matching & XML GT",
    description="Shows 4 views per detector. Original resolution kept; click on Gallery images to open full."
)

iface.launch()