import os
import numpy as np
from crop_image import crop_image, convert_png_to_jpg,draw_points_on_image,bytes_to_pil,validate_path
import time
from PIL import Image, ImageDraw
from io import BytesIO
import onnxruntime as ort


def predict(icon_image, bg_image):
    import torch
    from train import MyResNet18, data_transform
    current_dir = os.path.dirname(os.path.abspath(__file__))
    model_path = os.path.join(current_dir, 'model', 'resnet18_38_0.021147585306924.pth')
    coordinates = [
        [1, 1],
        [1, 2],
        [1, 3],
        [2, 1],
        [2, 2],
        [2, 3],
        [3, 1],
        [3, 2],
        [3, 3],
    ]
    target_images = []
    target_images.append(data_transform(Image.open(BytesIO(icon_image))))

    bg_images = crop_image(bg_image, coordinates)
    for bg_image in bg_images:
        target_images.append(data_transform(bg_image))

    start = time.time()
    model = MyResNet18(num_classes=91)  # 这里的类别数要与训练时一致
    model.load_state_dict(torch.load(model_path))
    model.eval()
    print("加载模型，耗时:", time.time() - start)
    start = time.time()

    target_images = torch.stack(target_images, dim=0)
    target_outputs = model(target_images)

    scores = []

    for i, out_put in enumerate(target_outputs):
        if i == 0:
            # 增加维度，以便于计算
            target_output = out_put.unsqueeze(0)
        else:
            similarity = torch.nn.functional.cosine_similarity(
                target_output, out_put.unsqueeze(0)
            )
            scores.append(similarity.cpu().item())
    # 从左到右，从上到下，依次为每张图片的置信度
    print(scores)
    # 对数组进行排序，保持下标
    indexed_arr = list(enumerate(scores))
    sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
    # 提取最大三个数及其下标
    largest_three = sorted_arr[:3]
    print(largest_three)
    print("识别完成，耗时:", time.time() - start)

def load_model(name='PP-HGNetV2-B4.onnx'):
    # 加载onnx模型
    global session,input_name
    start = time.time()
    current_dir = os.path.dirname(os.path.abspath(__file__))
    model_path = os.path.join(current_dir, 'model', name)
    session = ort.InferenceSession(model_path)
    input_name = session.get_inputs()[0].name
    print(f"加载{name}模型，耗时:{time.time() - start}")

def load_dfine_model(name='d-fine-n.onnx'):
    # 加载onnx模型
    global session_dfine
    start = time.time()
    current_dir = os.path.dirname(os.path.abspath(__file__))
    model_path = os.path.join(current_dir, 'model', name)
    session_dfine = ort.InferenceSession(model_path)
    print(f"加载{name}模型，耗时:{time.time() - start}")


def predict_onnx(icon_image, bg_image, point = None):
    import cv2
    coordinates = [
        [1, 1],
        [1, 2],
        [1, 3],
        [2, 1],
        [2, 2],
        [2, 3],
        [3, 1],
        [3, 2],
        [3, 3],
    ]

    def cosine_similarity(vec1, vec2):
        # 将输入转换为 NumPy 数组
        vec1 = np.array(vec1)
        vec2 = np.array(vec2)
        # 计算点积
        dot_product = np.dot(vec1, vec2)
        # 计算向量的范数
        norm_vec1 = np.linalg.norm(vec1)
        norm_vec2 = np.linalg.norm(vec2)
        # 计算余弦相似度
        similarity = dot_product / (norm_vec1 * norm_vec2)
        return similarity

    def data_transforms(image):
        image = image.resize((224, 224))
        image = Image.fromarray(cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2RGB))
        image_array = np.array(image)
        image_array = image_array.astype(np.float32) / 255.0
        mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
        std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
        image_array = (image_array - mean) / std
        image_array = np.transpose(image_array, (2, 0, 1))
        # image_array = np.expand_dims(image_array, axis=0)
        return image_array

    target_images = []
    target_images.append(data_transforms(Image.open(BytesIO(icon_image))))
    bg_images = crop_image(bg_image, coordinates)

    for one in bg_images:
        target_images.append(data_transforms(one))

    start = time.time()
    outputs = session.run(None, {input_name: target_images})[0]

    scores = []
    for i, out_put in enumerate(outputs):
        if i == 0:
            target_output = out_put
        else:
            similarity = cosine_similarity(target_output, out_put)
            scores.append(similarity)
    # 从左到右，从上到下，依次为每张图片的置信度
    # print(scores)
    # 对数组进行排序，保持下标
    indexed_arr = list(enumerate(scores))
    sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
    # 提取最大三个数及其下标
    if point == None:
        largest_three = sorted_arr[:3]
        answer = [coordinates[i[0]] for i in largest_three]
    # 基于分数判断
    else:
        answer = [one[0] for one in sorted_arr if one[1] > point]
    print(f"识别完成{answer}，耗时: {time.time() - start}")
    #draw_points_on_image(bg_image, answer)
    return answer

def predict_onnx_pdl(images_path):
    coordinates = [
        [1, 1],
        [1, 2],
        [1, 3],
        [2, 1],
        [2, 2],
        [2, 3],
        [3, 1],
        [3, 2],
        [3, 3],
    ]
    def data_transforms(path):
        # 打开图片
        img = Image.open(path)
        # 调整图片大小为232x224（假设最短边长度调整为232像素）
        if img.width < img.height:
            new_size = (232, int(232 * img.height / img.width))
        else:
            new_size = (int(232 * img.width / img.height), 232)
        resized_img = img.resize(new_size, Image.BICUBIC)
        # 裁剪图片为224x224
        cropped_img = resized_img.crop((0, 0, 224, 224))
        # 将图像转换为NumPy数组并进行归一化处理
        img_array = np.array(cropped_img).astype(np.float32)
        img_array /= 255.0
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        img_array -= np.array(mean)
        img_array /= np.array(std)
        # 将通道维度移到前面
        img_array = np.transpose(img_array, (2, 0, 1))
        return img_array
    images = []
    for pic in sorted(os.listdir(images_path)):
        if "cropped" not in pic:
            continue
        image_path = os.path.join(images_path,pic)
        images.append(data_transforms(image_path))
    if len(images) == 0:
        raise FileNotFoundError(f"先使用切图代码切图至{image_path}再推理,图片命名如cropped_9.jpg,从0到9共十个,最后一个是检测目标")
    start = time.time()
    outputs = session.run(None, {input_name: images})[0]
    result = [np.argmax(one) for one in outputs]
    target = result[-1]
    answer = [coordinates[index] for index in range(9) if result[index] == target]
    if len(answer) == 0:
        all_sort =[np.argsort(one) for one in outputs]
        answer = [coordinates[index] for index in range(9) if all_sort[index][1] == target]
    print(f"识别完成{answer}，耗时: {time.time() - start}")
    with open(os.path.join(images_path,"nine.jpg"),'rb') as f:
        bg_image = f.read()
    # draw_points_on_image(bg_image, answer)
    return answer
    
def calculate_iou(boxA, boxB):
    xA = np.maximum(boxA[0], boxB[0])
    yA = np.maximum(boxA[1], boxB[1])
    xB = np.minimum(boxA[2], boxB[2])
    yB = np.minimum(boxA[3], boxB[3])

    intersection_area = np.maximum(0, xB - xA) * np.maximum(0, yB - yA)
    boxA_area = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
    boxB_area = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
    union_area = float(boxA_area + boxB_area - intersection_area)
    if union_area == 0:
        return 0.0 
    iou = intersection_area / union_area
    return iou
def non_maximum_suppression(detections, iou_threshold=0.35):
    if not detections:
        return []
    detections.sort(key=lambda x: x['score'], reverse=True)

    final_detections = []
    while detections:
        best_detection = detections.pop(0)
        final_detections.append(best_detection)
        detections_to_keep = []
        for det in detections:
            iou = calculate_iou(best_detection['box'], det['box'])
            if iou < iou_threshold:
                detections_to_keep.append(det)
        detections = detections_to_keep

    return final_detections

def predict_onnx_dfine(image,draw_result=False):
    input_nodes = session_dfine.get_inputs()
    output_nodes = session_dfine.get_outputs()
    image_input_name = input_nodes[0].name
    size_input_name = input_nodes[1].name
    output_names = [node.name for node in output_nodes]
    if isinstance(image,bytes):
        im_pil = bytes_to_pil(image)
    else:
        im_pil = Image.open(image).convert("RGB")
    w, h = im_pil.size
    orig_size_np = np.array([[w, h]], dtype=np.int64)
    im_resized = im_pil.resize((320, 320), Image.Resampling.BILINEAR)
    im_data = np.array(im_resized, dtype=np.float32) / 255.0
    im_data = im_data.transpose(2, 0, 1)
    im_data = np.expand_dims(im_data, axis=0)
    inputs = {
        image_input_name: im_data,
        size_input_name: orig_size_np
    }
    outputs = session_dfine.run(output_names, inputs)
    output_map = {name: data for name, data in zip(output_names, outputs)}
    labels = output_map['labels'][0]
    boxes = output_map['boxes'][0]
    scores = output_map['scores'][0]

    colors = ["red", "blue", "green", "yellow", "white", "purple", "orange"]
    mask = scores > 0.4
    filtered_labels = labels[mask]
    filtered_boxes = boxes[mask]
    filtered_scores = scores[mask]

    rebuild_color = {}
    unique_labels = list(set(filtered_labels))
    for i, l_val in enumerate(unique_labels):
        class_id = int(l_val)
        if class_id not in rebuild_color:
            rebuild_color[class_id] = colors[i % len(colors)]
    result = {k: [] for k in unique_labels}
    for i, box in enumerate(filtered_boxes):
        if box[2]>160 and box[3] < 45:
            continue
        label_val = filtered_labels[i]
        class_id = int(label_val)
        color = rebuild_color[class_id]
        score = filtered_scores[i]
        
        result[class_id].append({
            'box': box,
            'label_val': label_val,
            'score': score
        })
    keep_result = {}
    result_points = []
    for class_id in result:
        tp = non_maximum_suppression(result[class_id],0.01)
        if len(tp) < 2:
            continue
        point = tp[0]["score"]+tp[1]["score"]
        if point < 0.85:
            continue
        keep_result.update({class_id:tp[0:2]})
        result_points.append({"id":class_id,"point":point})
    result_points.sort(key=lambda item: item['point'], reverse=True)
    if len(keep_result) > 3:
        tp = {}
        for one in result_points[0:3]:
            tp.update({one['id']:keep_result[one['id']]})
        keep_result = tp
    for class_id in keep_result:
        keep_result[class_id].sort(key=lambda item: item['box'][3], reverse=True)
    sorted_result = {}
    sorted_class_ids = sorted(keep_result.keys(), key=lambda cid: keep_result[cid][0]['box'][0])
    for class_id in sorted_class_ids:
        sorted_result[class_id] = keep_result[class_id]
    points = []

    if draw_result:
        draw = ImageDraw.Draw(im_pil)
    for c1,class_id in enumerate(sorted_result):
        items = sorted_result[class_id]
        last_item = items[-1]
        center_x = (last_item['box'][0] + last_item['box'][2]) / 2
        center_y = (last_item['box'][1] + last_item['box'][3]) / 2
        text_position_center = (center_x , center_y)
        points.append(text_position_center)
        if draw_result:
            color = rebuild_color[class_id]
            draw.point((center_x, center_y), fill=color)
            text_center = f"{c1}"
            draw.text(text_position_center, text_center, fill=color)
            for c2,item in enumerate(items):
                box = item['box']
                score = item['score']
                
                draw.rectangle(list(box), outline=color, width=1)
                text = f"{class_id}_{c1}-{c2}: {score:.2f}"
                text_position = (box[0] + 2, box[1] - 12 if box[1] > 12 else box[1] + 2)
                draw.text(text_position, text, fill=color)
    if draw_result:
       save_path = os.path.join(validate_path,"icon_result.jpg")
       im_pil.save(save_path)
       print(f"图片可视化结果保存在{save_path}")
    print(f"图片顺序的中心点{points}")
    return points
    

print(f"使用推理设备: {ort.get_device()}")
if int(os.environ.get("use_pdl",1)):
   load_model()
if int(os.environ.get("use_dfine",1)):
   load_dfine_model()
if __name__ == "__main__":
    # 使用resnet18.onnx
    # load_model("resnet18.onnx")
    # icon_path = "img_2_val/cropped_9.jpg"
    # bg_path = "img_2_val/nine.jpg"
    # with open(icon_path, "rb") as rb:
    #     if icon_path.endswith('.png'):
    #         icon_image = convert_png_to_jpg(rb.read())
    #     else:
    #         icon_image = rb.read()
    # with open(bg_path, "rb") as rb:
    #     bg_image = rb.read()
    # predict_onnx(icon_image, bg_image)
    
    # 使用PP-HGNetV2-B4.onnx
    #predict_onnx_pdl(r'img_saved\img_fail\7fe559a85bac4c03bc6ea7b2e85325bf')
    predict_onnx_dfine(r"n:\爬点选\dataset\3f98ff0c91dd4882a8a24d451283ad96.jpg",True)