💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
蘑菇种类识别系统的设计与实现介绍
蘑菇种类识别系统是一个基于大数据技术的智能识别平台，采用Python+Django作为后端开发框架，结合Hadoop+Spark大数据处理技术，实现对蘑菇图像的智能识别与分类。系统前端采用Vue+ElementUI+Echarts技术栈，为用户提供直观友好的操作界面。核心功能涵盖蘑菇图像识别、科普知识展示、分类管理、交流论坛、用户互动等模块。通过Spark进行大规模图像数据处理和特征提取，利用HDFS分布式存储海量蘑菇图像数据，结合Pandas、NumPy进行数据分析处理。系统支持用户上传蘑菇图片进行智能识别，提供详细的蘑菇科普信息，建立用户交流社区，实现知识分享与讨论。数据库采用MySQL存储用户信息、识别记录、论坛内容等结构化数据，整个系统架构完整，功能实用，技术先进，能够有效解决蘑菇种类识别的实际问题，为生物学研究和日常生活提供便利的识别工具。
蘑菇种类识别系统的设计与实现演示视频
蘑菇种类识别系统的设计与实现演示图片
蘑菇种类识别系统的设计与实现代码展示
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import cv2
import numpy as np
from PIL import Image
import base64
from io import BytesIO
from .models import MushroomInfo, ForumPost, KnowledgeBase
spark = SparkSession.builder.appName("MushroomRecognition").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def mushroom_recognition(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
image_data = data.get('image')
image_binary = base64.b64decode(image_data.split(',')[1])
image = Image.open(BytesIO(image_binary))
image_array = np.array(image)
gray_image = cv2.cvtColor(image_array, cv2.COLOR_RGB2GRAY)
features = cv2.calcHist([gray_image], [0], None, [256], [0, 256]).flatten()
color_features = np.mean(image_array.reshape(-1, 3), axis=0)
shape_contours, _ = cv2.findContours(gray_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if shape_contours:
largest_contour = max(shape_contours, key=cv2.contourArea)
area = cv2.contourArea(largest_contour)
perimeter = cv2.arcLength(largest_contour, True)
shape_ratio = area / (perimeter * perimeter) if perimeter > 0 else 0
else:
shape_ratio = 0
all_features = np.concatenate([features, color_features, [shape_ratio]])
spark_df = spark.createDataFrame([(all_features.tolist(),)], ['features'])
assembler = VectorAssembler(inputCols=['features'], outputCol='feature_vector')
feature_df = assembler.transform(spark_df)
model_path = '/path/to/trained_model'
rf_model = RandomForestClassifier.load(model_path)
predictions = rf_model.transform(feature_df)
prediction_result = predictions.select('prediction').collect()[0]['prediction']
mushroom_types = ['毒蘑菇', '可食用蘑菇', '药用蘑菇', '未知蘑菇']
predicted_type = mushroom_types[int(prediction_result)] if int(prediction_result) < len(mushroom_types) else '未知蘑菇'
confidence_scores = predictions.select('probability').collect()[0]['probability']
max_confidence = float(max(confidence_scores))
mushroom_info = MushroomInfo.objects.filter(category=predicted_type).first()
result_data = {
'predicted_type': predicted_type,
'confidence': max_confidence,
'description': mushroom_info.description if mushroom_info else '暂无详细信息',
'safety_level': mushroom_info.safety_level if mushroom_info else '未知',
'edible': mushroom_info.edible if mushroom_info else False
}
return JsonResponse({'status': 'success', 'data': result_data})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
return JsonResponse({'status': 'error', 'message': '请求方法不正确'})
@csrf_exempt
def knowledge_analysis(request):
if request.method == 'GET':
try:
knowledge_data = list(KnowledgeBase.objects.values('id', 'title', 'content', 'category', 'views', 'created_time'))
spark_df = spark.createDataFrame(knowledge_data)
category_stats = spark_df.groupBy('category').count().collect()
category_analysis = {row['category']: row['count'] for row in category_stats}
popular_knowledge = spark_df.orderBy(spark_df.views.desc()).limit(10).collect()
content_lengths = spark_df.rdd.map(lambda row: (row['id'], len(row['content']))).collect()
avg_content_length = sum([length for _, length in content_lengths]) / len(content_lengths)
monthly_creation = spark_df.groupBy(spark_df.created_time.substr(1, 7).alias('month')).count().collect()
monthly_stats = {row['month']: row['count'] for row in monthly_creation}
word_frequency_analysis = spark_df.rdd.flatMap(lambda row: row['content'].split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b).takeOrdered(20, key=lambda x: -x[1])
high_engagement_threshold = spark_df.agg({'views': 'avg'}).collect()[0][0] * 1.5
high_engagement_content = spark_df.filter(spark_df.views > high_engagement_threshold).collect()
category_engagement = spark_df.groupBy('category').agg({'views': 'avg', 'views': 'sum'}).collect()
trending_topics = []
for row in popular_knowledge[:5]:
trending_topics.append({
'title': row['title'],
'category': row['category'],
'views': row['views'],
'engagement_score': row['views'] / avg_content_length * len(row['content'])
})
analysis_result = {
'category_distribution': category_analysis,
'popular_content': [{'title': row['title'], 'views': row['views']} for row in popular_knowledge],
'average_content_length': avg_content_length,
'monthly_creation_trend': monthly_stats,
'top_keywords': word_frequency_analysis,
'high_engagement_content_count': len(high_engagement_content),
'trending_topics': trending_topics,
'total_knowledge_count': spark_df.count()
}
return JsonResponse({'status': 'success', 'data': analysis_result})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
return JsonResponse({'status': 'error', 'message': '请求方法不正确'})
@csrf_exempt
def forum_data_processing(request):
if request.method == 'POST':
try:
forum_posts = list(ForumPost.objects.values('id', 'title', 'content', 'author_id', 'category_id', 'views', 'replies', 'created_time', 'updated_time'))
spark_df = spark.createDataFrame(forum_posts)
user_activity_stats = spark_df.groupBy('author_id').agg({'id': 'count', 'views': 'sum', 'replies': 'sum'}).collect()
active_users = []
for row in user_activity_stats:
activity_score = row['sum(views)'] * 0.3 + row['sum(replies)'] * 0.7 + row['count(id)'] * 2
active_users.append({
'user_id': row['author_id'],
'post_count': row['count(id)'],
'total_views': row['sum(views)'],
'total_replies': row['sum(replies)'],
'activity_score': activity_score
})
active_users.sort(key=lambda x: x['activity_score'], reverse=True)
hot_posts = spark_df.rdd.map(lambda row: (row['id'], row['title'], row['views'] * 0.6 + row['replies'] * 0.4)).takeOrdered(15, key=lambda x: -x[2])
category_performance = spark_df.groupBy('category_id').agg({'views': 'avg', 'replies': 'avg', 'id': 'count'}).collect()
time_analysis = spark_df.rdd.map(lambda row: (row['created_time'].hour, 1)).reduceByKey(lambda a, b: a + b).collect()
peak_hours = sorted(time_analysis, key=lambda x: x[1], reverse=True)[:3]
content_quality_scores = spark_df.rdd.map(lambda row: (row['id'], len(row['content']) * 0.1 + row['replies'] * 2 + row['views'] * 0.05)).collect()
quality_threshold = sum([score for _, score in content_quality_scores]) / len(content_quality_scores)
high_quality_posts = [post_id for post_id, score in content_quality_scores if score > quality_threshold * 1.2]
engagement_trends = spark_df.groupBy(spark_df.created_time.substr(1, 10).alias('date')).agg({'views': 'sum', 'replies': 'sum', 'id': 'count'}).collect()
daily_engagement = []
for row in engagement_trends:
daily_engagement.append({
'date': row['date'],
'total_views': row['sum(views)'],
'total_replies': row['sum(replies)'],
'post_count': row['count(id)'],
'engagement_ratio': row['sum(replies)'] / row['sum(views)'] if row['sum(views)'] > 0 else 0
})
processing_result = {
'top_active_users': active_users[:10],
'hot_posts': [{'post_id': pid, 'title': title, 'heat_score': score} for pid, title, score in hot_posts],
'category_stats': [{'category_id': row['category_id'], 'avg_views': row['avg(views)'], 'avg_replies': row['avg(replies)'], 'post_count': row['count(id)']} for row in category_performance],
'peak_activity_hours': [{'hour': hour, 'post_count': count} for hour, count in peak_hours],
'high_quality_posts_count': len(high_quality_posts),
'daily_engagement_trends': daily_engagement[-30:],
'total_forum_activity': spark_df.count()
}
return JsonResponse({'status': 'success', 'data': processing_result})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
return JsonResponse({'status': 'error', 'message': '请求方法不正确'})
蘑菇种类识别系统的设计与实现文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
