import cv2
import numpy as np
import json
import gradio as gr

# ---------------- Your Original Functions (Unchanged) ---------------- #
def get_rotated_rect_corners(x, y, w, h, rotation_deg):
    rot_rad = np.deg2rad(rotation_deg)
    cos_r = np.cos(rot_rad)
    sin_r = np.sin(rot_rad)

    R = np.array([[cos_r, -sin_r],
                  [sin_r,  cos_r]])

    cx = x + w/2
    cy = 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)
    corners = rotated_corners + np.array([cx, cy])
    return corners.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":
        sift = cv2.SIFT_create(nfeatures=5000)
        kp1, des1 = sift.detectAndCompute(img1_gray, None)
        kp2, des2 = sift.detectAndCompute(img2_gray, None)
        matcher = cv2.BFMatcher(cv2.NORM_L2)
    elif method == "ORB":
        orb = cv2.ORB_create(5000)
        kp1, des1 = orb.detectAndCompute(img1_gray, None)
        kp2, des2 = orb.detectAndCompute(img2_gray, None)
        matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
    elif method == "BRISK":
        brisk = cv2.BRISK_create()
        kp1, des1 = brisk.detectAndCompute(img1_gray, None)
        kp2, des2 = brisk.detectAndCompute(img2_gray, None)
        matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
    elif method == "KAZE":
        kaze = cv2.KAZE_create()
        kp1, des1 = kaze.detectAndCompute(img1_gray, None)
        kp2, des2 = kaze.detectAndCompute(img2_gray, None)
        matcher = cv2.BFMatcher(cv2.NORM_L2)
    elif method == "AKAZE":
        akaze = cv2.AKAZE_create()
        kp1, des1 = akaze.detectAndCompute(img1_gray, None)
        kp2, des2 = akaze.detectAndCompute(img2_gray, None)
        matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
    else:
        return None, None, None

    raw_matches = matcher.knnMatch(des1, des2, k=2)
    good = []
    for m, n in raw_matches:
        if m.distance < ratio_thresh * n.distance:
            good.append(m)
    return kp1, kp2, good

# ---------------- Processing Function for Gradio ---------------- #
def homography_all_detectors(flat_img, persp_img, json_file):
    if flat_img is None or persp_img is None:
        return [None] * 6
    
    flat_bgr = cv2.cvtColor(flat_img, cv2.COLOR_RGB2BGR)
    persp_bgr = cv2.cvtColor(persp_img, cv2.COLOR_RGB2BGR)

    with open(json_file.name, 'r') as f:
        mockup = json.load(f)

    roi_data = mockup["printAreas"][0]["position"]
    roi_x = roi_data["x"]
    roi_y = roi_data["y"]
    roi_w = mockup["printAreas"][0]["width"]
    roi_h = mockup["printAreas"][0]["height"]
    roi_rot_deg = mockup["printAreas"][0]["rotation"]

    flat_gray = preprocess_gray_clahe(flat_bgr)
    persp_gray = preprocess_gray_clahe(persp_bgr)

    detectors = ["SIFT", "ORB", "BRISK", "KAZE", "AKAZE"]
    gallery_images = []
    download_files = [None] * 5

    for i, method in enumerate(detectors):
        kp1, kp2, good_matches = detect_and_match(flat_gray, persp_gray, method=method)
        if kp1 is None or kp2 is None or len(good_matches) < 4:
            continue
        
        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, mask = 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_debug = persp_bgr.copy()
        cv2.polylines(persp_debug, [roi_corners_persp.astype(int)], True, (0,255,0), 2)
        for (px, py) in roi_corners_persp:
            cv2.circle(persp_debug, (int(px), int(py)), 5, (255,0,0), -1)

        result_rgb = cv2.cvtColor(persp_debug, cv2.COLOR_BGR2RGB)
        file_name = f"result_{method.lower()}.png"
        cv2.imwrite(file_name, result_rgb[:, :, ::-1])  # save as BGR

        gallery_images.append((f"{method} Result", result_rgb))
        download_files[i] = file_name

    return [gallery_images] + download_files

# ---------------- Gradio Interface ---------------- #
iface = gr.Interface(
    fn=homography_all_detectors,
    inputs=[
        gr.Image(type="numpy", label="Image 1 (Flat)"),
        gr.Image(type="numpy", label="Image 2 (Perspective)"),
        gr.File(type="filepath", label="JSON File")
    ],
    outputs=[
        gr.Gallery(label="Results"),
        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 Multiple Feature Detectors",
    description="Upload a flat image, a perspective image, and the JSON file. The system will compute homography with SIFT, ORB, BRISK, KAZE, and AKAZE, project the bounding box, and allow result download."
)

iface.launch()