Upload agents/optical_agent.py with huggingface_hub

agents/optical_agent.py

@@ -37,10 +37,10 @@ def f01_ca_magnitude(img: Image.Image) -> Dict[str, Any]:
     rb = float(np.corrcoef(er[::step],eb[::step])[0,1])
     gb = float(np.corrcoef(eg[::step],eb[::step])[0,1])
     avg = (rg+rb+gb)/3
-    if avg > 0.99:   s,n = 0.6, "Perfect channel alignment — no natural CA"
+    if avg > 0.99:   s,n = 0.35, "Near-perfect channel alignment — weak CA indicator (modern diffusion models can produce CA)"
     elif avg < 0.70:  s,n = 0.5, "Abnormally low channel correlation"
     elif 0.80<=avg<=0.97: s,n = -0.4, "Natural CA pattern detected"
-    else: s,n = 0.2, "Borderline CA"
+    else: s,n = 0.15, "Borderline CA"
     return {"test":"CA Magnitude","avg_corr":round(avg,4),"score":s,"note":n}
 
 # ── 2. CA Radial Gradient ────────────────────────────────────────────
@@ -103,8 +103,8 @@ def f05_vignetting_symmetry(img: Image.Image) -> Dict[str, Any]:
     q3 = float(np.mean(gray[h//2:,:w//2])); q4 = float(np.mean(gray[h//2:,w//2:]))
     qs = [q1,q2,q3,q4]; std = float(np.std(qs)); mean = float(np.mean(qs))
     asym = std/(mean+1e-9)
-    if asym < 0.03: s,n = -0.2, f"Symmetric brightness (asym={asym:.4f}) — real optics"
-    elif asym > 0.1: s,n = 0.3, f"Asymmetric brightness (asym={asym:.4f}) — manipulation"
+    if asym < 0.03: s,n = -0.15, f"Symmetric brightness (asym={asym:.4f}) — real optics"
+    elif asym > 0.2: s,n = 0.15, f"Asymmetric brightness (asym={asym:.4f}) — possible manipulation (but could be scene lighting)"
     else: s,n = 0.0, f"Moderate asymmetry ({asym:.4f})"
     return {"test":"Vignetting Symmetry","asymmetry":round(asym,4),"score":s,"note":n}
 
@@ -381,9 +381,9 @@ def f20_optical_center(img: Image.Image) -> Dict[str, Any]:
     cy, cx = h/2, w/2
     offset_y = abs(y_max - cy)/(h+1e-9); offset_x = abs(x_max - cx)/(w+1e-9)
     offset = np.sqrt(offset_y**2 + offset_x**2)
-    if offset < 0.1: s,n = -0.2, f"Optical center near image center (offset={offset:.3f})"
+    if offset < 0.1: s,n = -0.1, f"Optical center near image center (offset={offset:.3f})"
     elif offset < 0.25: s,n = 0.0, f"Slight optical center offset ({offset:.3f})"
-    else: s,n = 0.2, f"Optical center far from center ({offset:.3f})"
+    else: s,n = 0.1, f"Optical center offset ({offset:.3f}) — unreliable for scenes with bright objects"
     return {"test":"Optical Center","offset":round(offset,4),"score":s,"note":n}
 
 
@@ -395,20 +395,6 @@ ALL_TESTS = [f01_ca_magnitude,f02_ca_radial,f03_lateral_ca,f04_vignetting,
              f17_sharpness_falloff,f18_diffraction,f19_geometric_distortion,f20_optical_center]
 
 def run_optical_agent(img: Image.Image) -> AgentEvidence:
-    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]
-    if viol: rat = f"Optical violations: {', '.join(viol)}."
-    elif comp: rat = f"Optical physics consistent: {', '.join(comp)}."
-    else: rat = "Optical analysis inconclusive."
-    for f in findings:
-        if f.get("note"): rat += f" [{f['test']}]: {f['note']}."
-    return AgentEvidence("Optical Physics Agent", np.clip(avg,-1,1), conf,
-                         max(0.0,1.0-len(scores)/len(ALL_TESTS)), rat, findings)
+    from agents.utils import run_agent_tests
+    findings, avg, conf, fail, rat = run_agent_tests(ALL_TESTS, img, "Optical Physics Agent")
+    return AgentEvidence("Optical Physics Agent", np.clip(avg,-1,1), conf, fail, rat, findings)