mirror of
https://github.com/luguoyixiazi/test_nine.git
synced 2025-12-05 14:42:49 +08:00
221 lines
7.4 KiB
Python
221 lines
7.4 KiB
Python
import os
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import numpy as np
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from train import MyResNet18, data_transform
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from crop_image import crop_image, convert_png_to_jpg,draw_points_on_image
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import torch
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import time
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import cv2
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from PIL import Image
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from io import BytesIO
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import onnxruntime as ort
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def predict(icon_image, bg_image):
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', 'resnet18_38_0.021147585306924.pth')
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coordinates = [
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[1, 1],
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[1, 2],
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[1, 3],
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[2, 1],
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[2, 2],
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[2, 3],
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[3, 1],
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[3, 2],
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[3, 3],
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]
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target_images = []
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target_images.append(data_transform(Image.open(BytesIO(icon_image))))
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bg_images = crop_image(bg_image, coordinates)
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for bg_image in bg_images:
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target_images.append(data_transform(bg_image))
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start = time.time()
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model = MyResNet18(num_classes=91) # 这里的类别数要与训练时一致
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model.load_state_dict(torch.load(model_path))
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model.eval()
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print("加载模型，耗时:", time.time() - start)
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start = time.time()
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target_images = torch.stack(target_images, dim=0)
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target_outputs = model(target_images)
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scores = []
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for i, out_put in enumerate(target_outputs):
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if i == 0:
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# 增加维度，以便于计算
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target_output = out_put.unsqueeze(0)
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else:
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similarity = torch.nn.functional.cosine_similarity(
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target_output, out_put.unsqueeze(0)
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)
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scores.append(similarity.cpu().item())
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# 从左到右，从上到下，依次为每张图片的置信度
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print(scores)
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# 对数组进行排序，保持下标
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indexed_arr = list(enumerate(scores))
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sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
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# 提取最大三个数及其下标
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largest_three = sorted_arr[:3]
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print(largest_three)
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print("识别完成，耗时:", time.time() - start)
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def load_model(name='PP-HGNetV2-B4.onnx'):
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# 加载onnx模型
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global session,input_name
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start = time.time()
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current_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(current_dir, 'model', name)
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session = ort.InferenceSession(model_path)
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input_name = session.get_inputs()[0].name
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print("加载模型，耗时:", time.time() - start)
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def predict_onnx(icon_image, bg_image, point = None):
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coordinates = [
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[1, 1],
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[1, 2],
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[1, 3],
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[2, 1],
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[2, 2],
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[2, 3],
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[3, 1],
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[3, 2],
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[3, 3],
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]
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def cosine_similarity(vec1, vec2):
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# 将输入转换为 NumPy 数组
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vec1 = np.array(vec1)
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vec2 = np.array(vec2)
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# 计算点积
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dot_product = np.dot(vec1, vec2)
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# 计算向量的范数
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norm_vec1 = np.linalg.norm(vec1)
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norm_vec2 = np.linalg.norm(vec2)
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# 计算余弦相似度
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similarity = dot_product / (norm_vec1 * norm_vec2)
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return similarity
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def data_transforms(image):
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image = image.resize((224, 224))
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image = Image.fromarray(cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2RGB))
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image_array = np.array(image)
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image_array = image_array.astype(np.float32) / 255.0
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mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
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std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
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image_array = (image_array - mean) / std
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image_array = np.transpose(image_array, (2, 0, 1))
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# image_array = np.expand_dims(image_array, axis=0)
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return image_array
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target_images = []
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target_images.append(data_transforms(Image.open(BytesIO(icon_image))))
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bg_images = crop_image(bg_image, coordinates)
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for one in bg_images:
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target_images.append(data_transforms(one))
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start = time.time()
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outputs = session.run(None, {input_name: target_images})[0]
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scores = []
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for i, out_put in enumerate(outputs):
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if i == 0:
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target_output = out_put
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else:
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similarity = cosine_similarity(target_output, out_put)
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scores.append(similarity)
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# 从左到右，从上到下，依次为每张图片的置信度
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# print(scores)
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# 对数组进行排序，保持下标
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indexed_arr = list(enumerate(scores))
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sorted_arr = sorted(indexed_arr, key=lambda x: x[1], reverse=True)
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# 提取最大三个数及其下标
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if point == None:
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largest_three = sorted_arr[:3]
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answer = [coordinates[i[0]] for i in largest_three]
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# 基于分数判断
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else:
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answer = [one[0] for one in sorted_arr if one[1] > point]
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print(f"识别完成{answer}，耗时: {time.time() - start}")
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draw_points_on_image(bg_image, answer)
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return answer
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def predict_onnx_pdl(images_path):
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coordinates = [
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[1, 1],
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[1, 2],
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[1, 3],
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[2, 1],
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[2, 2],
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[2, 3],
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[3, 1],
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[3, 2],
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[3, 3],
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]
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def data_transforms(path):
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# 打开图片
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img = Image.open(path)
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# 调整图片大小为232x224（假设最短边长度调整为232像素）
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if img.width < img.height:
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new_size = (232, int(232 * img.height / img.width))
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else:
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new_size = (int(232 * img.width / img.height), 232)
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resized_img = img.resize(new_size, Image.BICUBIC)
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# 裁剪图片为224x224
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cropped_img = resized_img.crop((0, 0, 224, 224))
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# 将图像转换为NumPy数组并进行归一化处理
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img_array = np.array(cropped_img).astype(np.float32)
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img_array /= 255.0
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mean = [0.485, 0.456, 0.406]
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std = [0.229, 0.224, 0.225]
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img_array -= np.array(mean)
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img_array /= np.array(std)
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# 将通道维度移到前面
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img_array = np.transpose(img_array, (2, 0, 1))
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return img_array
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images = []
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for pic in sorted(os.listdir(images_path)):
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if "cropped" not in pic:
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continue
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image_path = os.path.join(images_path,pic)
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images.append(data_transforms(image_path))
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if len(images) == 0:
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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")
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start = time.time()
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outputs = session.run(None, {input_name: images})[0]
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result = [np.argmax(one) for one in outputs]
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target = result[-1]
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answer = [coordinates[index] for index in range(9) if result[index] == target]
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print(f"识别完成{answer}，耗时: {time.time() - start}")
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with open(os.path.join(images_path,"nine.jpg"),'rb') as f:
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bg_image = f.read()
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draw_points_on_image(bg_image, answer)
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return answer
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if __name__ == "__main__":
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# 使用resnet18.onnx
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# load_model("resnet18.onnx")
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# icon_path = "img_2_val/cropped_9.jpg"
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# bg_path = "img_2_val/nine.jpg"
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# with open(icon_path, "rb") as rb:
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# if icon_path.endswith('.png'):
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# icon_image = convert_png_to_jpg(rb.read())
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# else:
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# icon_image = rb.read()
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# with open(bg_path, "rb") as rb:
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# bg_image = rb.read()
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# predict_onnx(icon_image, bg_image)
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# 使用PP-HGNetV2-B4.onnx
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load_model()
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predict_onnx_pdl(r'img_saved\img_fail\7fe559a85bac4c03bc6ea7b2e85325bf')
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else:
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load_model() |