feat: Flask Web UI — 在线cDNA图像处理平台
- 上传图像 + 实时处理 + 6张结果可视化 - 实验室仪器风格深色主题 - 参数统计面板（T/pct/网格/斑点数） - 图片点击放大 + 单张/全部下载
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"""
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cDNA微阵列图像处理 - Web UI (Flask)
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=====================================
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启动：python web/app.py
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打开：http://localhost:5000
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"""
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import os, sys, io, base64
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from flask import Flask, render_template, request, jsonify
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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from skimage import color
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from scipy import ndimage
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# 项目根目录加到 sys.path
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BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, os.path.join(BASE_DIR, 'src'))
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app = Flask(__name__)
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app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024 # 50MB
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UPLOAD_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'uploads')
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
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plt.rcParams['axes.unicode_minus'] = False
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# ================================================================
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# 图像处理函数（同简化版逻辑）
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# ================================================================
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def otsu_threshold_pixels(gray):
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best_T, best_cost, total = 0, float('inf'), gray.size
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for T in range(1, 255):
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bg, fg = gray[gray <= T], gray[gray > T]
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if len(bg) == 0 or len(fg) == 0:
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continue
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cost = len(bg)/total*np.var(bg) + len(fg)/total*np.var(fg)
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if cost < best_cost:
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best_cost, best_T = cost, T
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return best_T
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def draw_grid_lines(gray):
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T = otsu_threshold_pixels(gray)
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pct = T / 255.0
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H, W = gray.shape
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col_prof = np.sum(gray, axis=0).astype(float)
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row_prof = np.sum(gray, axis=1).astype(float)
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col_T = (np.max(col_prof)-np.min(col_prof))*pct
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row_T = (np.max(row_prof)-np.min(row_prof))*pct
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col_s, row_s = col_prof-col_T, row_prof-row_T
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def find_gap_lines(prof):
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is_pos = prof > 0
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crossings = [i for i in range(1, len(is_pos)) if is_pos[i] != is_pos[i-1]]
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if len(crossings) < 2:
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return np.array([])
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start = 1 if not is_pos[0] else 0
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return np.array([int((crossings[k]+crossings[k+1])/2) for k in range(start, len(crossings)-1, 2)])
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xl = find_gap_lines(col_s)
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yl = find_gap_lines(row_s)
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return xl, yl, T, pct, col_prof, row_prof, col_s, row_s, col_T, row_T
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def keep_largest_object(binary):
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L, n = ndimage.label(binary)
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if n == 0: return np.zeros_like(binary)
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return (L == (np.argmax([int(np.sum(L==i)) for i in range(1,n+1)])+1)).astype(np.uint8)
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def remove_small_objects(binary):
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L, n = ndimage.label(binary)
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if n == 0: return binary
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areas = [int(np.sum(L==i)) for i in range(1,n+1)]
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minsz = max(1, int(np.median(areas)*0.25))
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r = binary.copy()
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for i in range(1, n+1):
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if areas[i-1] < minsz: r[L==i] = 0
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return r
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def fig_to_base64(fig):
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"""matplotlib figure → base64 PNG"""
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buf = io.BytesIO()
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fig.savefig(buf, format='png', dpi=120, bbox_inches='tight')
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buf.seek(0)
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b64 = base64.b64encode(buf.read()).decode()
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plt.close(fig)
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return f'data:image/png;base64,{b64}'
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def process_image(img_array):
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"""对上传的图像运行完整处理流程，返回 dict"""
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# 转灰度
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if img_array.ndim == 3 and img_array.shape[2] >= 3:
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gray = (color.rgb2gray(img_array[:,:,:3])*255).astype(np.uint8)
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else:
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gray = img_array.astype(np.uint8)
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# 网格划线
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xl, yl, T, pct, cp, rp, cs, rs, cT, rT = draw_grid_lines(gray)
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# 逐格分割
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bw = np.zeros_like(gray)
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for i in range(len(yl)-1):
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for j in range(len(xl)-1):
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r1, r2 = yl[i], yl[i+1]
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c1, c2 = xl[j], xl[j+1]
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blk = gray[r1:r2, c1:c2]
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if blk.size == 0: continue
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bt = otsu_threshold_pixels(blk)
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bb = keep_largest_object((blk > bt).astype(np.uint8))
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bw[r1:r2, c1:c2] = bb
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bw_clean = remove_small_objects(bw)
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# 统计
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L, n = ndimage.label(bw_clean)
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spots = [int(np.sum(L==i)) for i in range(1,n+1)]
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valid = [s for s in spots if s >= 10]
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# ---- 生成6张图 ----
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images = {}
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# 1: grid overlay
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fig, ax = plt.subplots(figsize=(6,6))
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ax.imshow(gray, cmap='gray')
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for x in xl: ax.axvline(x=x, color='lime', linewidth=0.5)
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for y in yl: ax.axhline(y=y, color='lime', linewidth=0.5)
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ax.set_title(f'Grid ({len(xl)}x{len(yl)})', fontsize=12); ax.axis('off')
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images['grid_overlay'] = fig_to_base64(fig)
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# 2: col projection
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fig, ax = plt.subplots(figsize=(10,4))
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xs = np.arange(len(cp)); ax.plot(xs,cp,'b-',lw=0.6); ax.axhline(y=cT,color='orange',ls='--',lw=1)
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ax.plot(xs,cs,'g-',lw=0.6,alpha=0.5)
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ax.fill_between(xs,0,cs,where=(cs>0),color='green',alpha=0.1)
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ax.fill_between(xs,0,cs,where=(cs<0),color='red',alpha=0.1)
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for x in xl: ax.axvline(x=x,color='red',lw=0.5,alpha=0.5)
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ax.set_title('Column Projection', fontsize=12); ax.set_xlabel('column')
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images['col_projection'] = fig_to_base64(fig)
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# 3: row projection
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fig, ax = plt.subplots(figsize=(10,4))
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ys = np.arange(len(rp)); ax.plot(rp,ys,'b-',lw=0.6); ax.axvline(x=rT,color='orange',ls='--',lw=1)
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ax.plot(rs,ys,'g-',lw=0.6,alpha=0.5)
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for y in yl: ax.axhline(y=y,color='red',lw=0.5,alpha=0.5)
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ax.set_title('Row Projection', fontsize=12); ax.set_ylabel('row')
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images['row_projection'] = fig_to_base64(fig)
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# 4: histogram
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fig, ax = plt.subplots(figsize=(7,4))
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ax.hist(gray.ravel(),bins=50,color='#2d8a4e',edgecolor='white',linewidth=0.3)
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ax.axvline(x=T,color='#ff4444',ls='--',lw=2,label=f'Otsu T={T} ({pct*100:.1f}%)')
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ax.set_title('Histogram + Otsu', fontsize=12); ax.legend()
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images['histogram'] = fig_to_base64(fig)
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# 5: segmentation raw
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fig, ax = plt.subplots(figsize=(6,6))
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ax.imshow(bw, cmap='gray'); ax.set_title('Segmentation (raw)', fontsize=12); ax.axis('off')
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images['segmentation_raw'] = fig_to_base64(fig)
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# 6: post processed
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fig, ax = plt.subplots(figsize=(6,6))
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ax.imshow(bw_clean, cmap='gray')
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ax.set_title(f'Post-processed ({len(valid)} spots)', fontsize=12); ax.axis('off')
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images['post_processed'] = fig_to_base64(fig)
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stats = {
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'spots': len(valid),
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'T_otsu': int(T),
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'pct': round(pct*100, 1),
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'lines_x': int(len(xl)),
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'lines_y': int(len(yl)),
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'width': int(gray.shape[1]),
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'height': int(gray.shape[0])
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}
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return {'images': images, 'stats': stats}
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# ================================================================
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# Flask 路由
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# ================================================================
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/process', methods=['POST'])
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def process():
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if 'file' not in request.files:
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return jsonify({'error': '未找到文件'}), 400
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file = request.files['file']
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if file.filename == '':
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return jsonify({'error': '文件名为空'}), 400
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# 读取图像
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img_bytes = file.read()
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img = Image.open(io.BytesIO(img_bytes))
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img_array = np.array(img)
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# 处理
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try:
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result = process_image(img_array)
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return jsonify(result)
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except Exception as e:
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return jsonify({'error': f'处理失败: {str(e)}'}), 500
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if __name__ == '__main__':
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app.run(debug=True, port=5000)
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