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agents/statistical_agent.py

@@ -137,15 +137,12 @@ def t09_lbp_distribution(img):
     return {"test":"LBP Distribution","uniform_ratio":round(uniform,4),"score":s,"note":n}
 
 def t10_cooccurrence(img):
-    gray=(np.array(img.convert("L"))//16).astype(int); h,w=gray.shape  # Quantize to 16 levels
+    gray=(np.array(img.convert("L"))//16).astype(int); h,w=gray.shape
+    # Vectorized GLCM — horizontal adjacency
     glcm=np.zeros((16,16))
-    for i in range(h):
-        for j in range(w-1):
-            glcm[gray[i,j],gray[i,j+1]]+=1
+    np.add.at(glcm, (gray[:,:-1].ravel(), gray[:,1:].ravel()), 1)
     glcm/=(glcm.sum()+1e-9)
-    # Energy (angular second moment)
     energy=float(np.sum(glcm**2))
-    # Homogeneity
     I,J=np.mgrid[0:16,0:16]; homog=float(np.sum(glcm/(1+np.abs(I-J))))
     if energy<0.05 and homog>0.5: s,n=-0.2,f"Natural texture (E={energy:.4f}, H={homog:.3f})"
     elif energy>0.2: s,n=0.3,f"Flat/repetitive (E={energy:.4f})"
@@ -276,13 +273,26 @@ def t21_joint_histogram(img):
     return {"test":"Joint Color Histogram","mi":round(mi,4),"score":s,"note":n}
 
 def t22_run_length(img):
-    gray=np.array(img.convert("L")); row=gray[gray.shape[0]//2,:]
-    runs=[]; cur=1
-    for i in range(1,len(row)):
-        if row[i]==row[i-1]: cur+=1
-        else: runs.append(cur); cur=1
-    runs.append(cur); runs=np.array(runs)
-    avg_run=float(np.mean(runs)); max_run=int(np.max(runs))
+    gray=np.array(img.convert("L")); h,w=gray.shape
+    # Sample 10 rows and 10 columns spread across the image
+    all_runs=[]
+    row_indices = np.linspace(0, h-1, min(10, h), dtype=int)
+    col_indices = np.linspace(0, w-1, min(10, w), dtype=int)
+    for ri in row_indices:
+        row=gray[ri,:]; cur=1
+        for i in range(1,len(row)):
+            if row[i]==row[i-1]: cur+=1
+            else: all_runs.append(cur); cur=1
+        all_runs.append(cur)
+    for ci in col_indices:
+        col=gray[:,ci]; cur=1
+        for i in range(1,len(col)):
+            if col[i]==col[i-1]: cur+=1
+            else: all_runs.append(cur); cur=1
+        all_runs.append(cur)
+    runs=np.array(all_runs)
+    if len(runs)<10: return {"test":"Run Length Analysis","score":0.0,"note":"Insufficient data"}
+    avg_run=float(np.mean(runs))
     if 1<avg_run<5: s,n=-0.2,f"Natural run lengths (avg={avg_run:.2f})"
     elif avg_run>10: s,n=0.3,f"Long runs ({avg_run:.2f}) — flat patches"
     else: s,n=0.0,f"Run avg={avg_run:.2f}"
@@ -295,14 +305,7 @@ ALL_TESTS=[t01_dct_kurtosis,t02_benford,t03_gradient_sparsity,t04_local_kurtosis
            t19_power_law_fit,t20_contrast_distribution,t21_joint_histogram,t22_run_length]
 
 def run_statistical_agent(img):
-    findings,scores=[],[]
-    for fn in ALL_TESTS:
-        try: r=fn(img); findings.append(r); scores.append(r["score"])
-        except Exception as e: findings.append({"test":fn.__name__,"error":str(e),"score":0})
-    avg=float(np.mean(scores)) if scores else 0.0; conf=min(1.0,0.5+0.5*abs(avg))
-    viol=[f["test"] for f in findings if f.get("score",0)>0.2]
-    comp=[f["test"] for f in findings if f.get("score",0)<-0.1]
-    rat=f"Statistical violations: {', '.join(viol)}." if viol else f"Natural statistics: {', '.join(comp)}." if comp else "Statistical inconclusive."
-    for f in findings:
-        if f.get("note"): rat+=f" [{f['test']}]: {f['note']}."
-    return AgentEvidence("Statistical Priors Agent",np.clip(avg,-1,1),conf,max(0,1-len(scores)/len(ALL_TESTS)),rat,findings)
+    from agents.utils import run_agent_tests
+    from agents.optical_agent import AgentEvidence
+    findings, avg, conf, fail, rat = run_agent_tests(ALL_TESTS, img, "Statistical Priors Agent")
+    return AgentEvidence("Statistical Priors Agent", np.clip(avg,-1,1), conf, fail, rat, findings)