Update main.py

main.py

@@ -13,16 +13,18 @@ from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 
-# OOM PREVENTION 1: Force PyTorch to use minimal memory overhead
+# OOM PREVENTION
 torch.set_num_threads(1)
 
 # Import your helpers
 from cv_helpers import blend_mask_overlays, stem_tip_tangent_deg
+import color_calibration as calib
+import test_color_eval as eval
 
 # --- CONFIGURATION ---
 MODEL_PATH = "best.pt"
-PIXELS_TO_CM = 1.0  
 MAX_IMAGE_SIZE = 1024  
+CHECKER_WIDTH_MM = 63.0  # Physical width of the ColorChecker
 
 @dataclass
 class ProcessResult:
@@ -32,12 +34,28 @@ class ProcessResult:
     width_val: Optional[float] = None
     height_val: Optional[float] = None
     perimeter_val: Optional[float] = None
+    delta_e_initial: Optional[float] = None
+    delta_e_final: Optional[float] = None
     image_base64: Optional[str] = None
     filename: Optional[str] = None
 
 class WatermelonProcessor:
     def __init__(self, model_path: str):
         self.model = YOLO(model_path)
+        
+        # Load the Golden Reference once on startup
+        self.ref24 = None
+        if os.path.exists("reference.png"):
+            try:
+                ref_img = cv2.imread("reference.png")
+                ref_corners = calib.detect_checker_corners(ref_img)
+                ref_warped = calib.warp_checker(ref_img, ref_corners)
+                self.ref24 = calib.sample_24_patches(ref_warped)
+                print("Reference ColorChecker loaded successfully.")
+            except Exception as e:
+                print(f"Failed to extract reference patches: {e}")
+        else:
+            print("WARNING: 'reference.png' not found. Calibration will be skipped.")
 
     @staticmethod
     def watermelon_model(theta, Rx, Ry, c_a, d_top, w_top, d_bot, w_bot, phi, c_skew, c_bend):
@@ -52,7 +70,6 @@ class WatermelonProcessor:
     def get_stable_perimeter_data(rind_mask, flesh_mask):
         cnts, _ = cv2.findContours(rind_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
         if not cnts: return None
-        
         best_cnt = None
         max_overlap = -1
         for cnt in cnts:
@@ -62,7 +79,6 @@ class WatermelonProcessor:
             if overlap_area > max_overlap:
                 max_overlap = overlap_area
                 best_cnt = cnt
-                
         if best_cnt is None: best_cnt = max(cnts, key=cv2.contourArea)
         moments = cv2.moments(best_cnt)
         if moments["m00"] == 0: return None
@@ -86,8 +102,8 @@ class WatermelonProcessor:
         return final_theta, final_r, (cx, cy), best_cnt
 
     @staticmethod
-    def get_dual_mask_midline(f_left, f_right, rind_cnt, predicted_cnt, cx, cy):
-        h, w = f_left.shape
+    def get_ray_scan_midline(flesh_mask, rind_cnt, predicted_cnt, cx, cy):
+        h, w = flesh_mask.shape
         if len(rind_cnt) > 5:
             _, (ma, Ma), angle = cv2.fitEllipse(rind_cnt)
             rot_angle = angle if ma < Ma else angle + 90
@@ -95,46 +111,33 @@ class WatermelonProcessor:
 
         m_rot = cv2.getRotationMatrix2D((cx, cy), rot_angle, 1.0)
         m_inv = cv2.getRotationMatrix2D((cx, cy), -rot_angle, 1.0)
-        
-        l_rot = cv2.warpAffine(f_left, m_rot, (w, h))
-        r_rot = cv2.warpAffine(f_right, m_rot, (w, h))
-
-        # Failsafe: Swap left/right if YOLO got labels crossed
-        l_idx = np.where(l_rot > 0)[1]
-        r_idx = np.where(r_rot > 0)[1]
-        if len(l_idx) > 0 and len(r_idx) > 0:
-            if np.mean(l_idx) > np.mean(r_idx):
-                l_rot, r_rot = r_rot, l_rot 
+        f_rot = cv2.warpAffine(flesh_mask, m_rot, (w, h))
 
         gap_points =[]
-        y_l = np.where(l_rot > 0)[0]
-        y_r = np.where(r_rot > 0)[0]
-        
-        if len(y_l) > 0 and len(y_r) > 0:
-            y_min = max(np.min(y_l), np.min(y_r))
-            y_max = min(np.max(y_l), np.max(y_r))
+        y_indices, _ = np.where(f_rot > 0)
+        if len(y_indices) > 0:
+            y_min, y_max = np.min(y_indices), np.max(y_indices)
             for y in range(y_min, y_max):
-                row_l = np.where(l_rot[y, :] > 0)[0]
-                row_r = np.where(r_rot[y, :] > 0)[0]
-                if len(row_l) > 0 and len(row_r) > 0:
-                    edge_l = row_l[-1]
-                    edge_r = row_r[0]
-                    gap_points.append([y, (edge_l + edge_r) / 2.0])
+                row = f_rot[y, :]
+                white_px = np.where(row > 0)[0]
+                if len(white_px) >= 2:
+                    first, last = white_px[0], white_px[-1]
+                    blanks_in_between = np.where(row[first:last] == 0)[0] + first
+                    if len(blanks_in_between) > 0:
+                        gap_points.append([y, np.median(blanks_in_between)])
 
         gap_points = np.array(gap_points)
         if len(gap_points) > 10:
-            y_min_g, y_max_g = np.min(gap_points[:, 0]), np.max(gap_points[:, 0])
-            y_span = max(y_max_g - y_min_g, 1)
-            y_mean = (y_max_g + y_min_g) / 2.0
+            y_min, y_max = np.min(gap_points[:, 0]), np.max(gap_points[:, 0])
+            y_span, y_mean = max(y_max - y_min, 1), (y_max + y_min) / 2.0
             y_norm = (gap_points[:, 0] - y_mean) / y_span
             x_data = gap_points[:, 1]
 
             def parabola(y_n, a, b, c): return a * (y_n**2) + b * y_n + c
-            max_bend = w * 0.08 
-            try:
-                popt_mid, _ = curve_fit(parabola, y_norm, x_data, bounds=([-max_bend, -np.inf, -np.inf],[max_bend, np.inf, np.inf]))
-            except:
-                popt_mid =[0.0, 0.0, cx]
+
+            max_bend = w * 0.08
+            try: popt_mid, _ = curve_fit(parabola, y_norm, x_data, bounds=([-max_bend, -np.inf, -np.inf], [max_bend, np.inf, np.inf]))
+            except: popt_mid = [0.0, 0.0, cx]
 
             ys_extrap = np.linspace(0, h, 500)
             ys_extrap_norm = (ys_extrap - y_mean) / y_span
@@ -142,7 +145,8 @@ class WatermelonProcessor:
             pts_rot = np.vstack([xs_extrap, ys_extrap, np.ones_like(xs_extrap)])
         else:
             ys_extrap = np.linspace(0, h, 500)
-            pts_rot = np.vstack([np.full_like(ys_extrap, cx), ys_extrap, np.ones_like(ys_extrap)])
+            xs_extrap = np.full_like(ys_extrap, cx)
+            pts_rot = np.vstack([xs_extrap, ys_extrap, np.ones_like(xs_extrap)])
 
         pts_orig = (m_inv @ pts_rot).T
         pred_cnt_cv = predicted_cnt.reshape(-1, 1, 2).astype(np.int32)
@@ -151,36 +155,55 @@ class WatermelonProcessor:
     def process_image(self, image: np.ndarray, source_name: str, scale_ratio: float) -> ProcessResult:
         if image is None: return ProcessResult(success=False, message="Could not decode image.")
         h, w = image.shape[:2]
+
+        # --- CALIBRATION & SCALING ---
+        dE_initial, dE_final, mm_per_px, checker_corners = None, None, None, None
         
-        # retina_masks=True removes the plateau artifacts during inference!
-        results = self.model(image, conf=0.25, retina_masks=True, verbose=False)
+        if self.ref24 is not None:
+            try:
+                # 1. Detect Checker
+                checker_corners = calib.detect_checker_corners(image)
+                
+                # 2. Calculate mm/px Scale
+                # Corners:[Bottom-Left, Top-Left, Top-Right, Bottom-Right]
+                top_width = np.linalg.norm(checker_corners[1] - checker_corners[2])
+                bot_width = np.linalg.norm(checker_corners[0] - checker_corners[3])
+                px_width = (top_width + bot_width) / 2.0
+                mm_per_px = CHECKER_WIDTH_MM / px_width
+                
+                # 3. Apply Calibration Pipeline
+                tgt_warped = calib.warp_checker(image, checker_corners)
+                tgt24 = calib.sample_24_patches(tgt_warped)
+                
+                dE_initial = float(np.mean(eval.compute_deltaE_00(tgt24, self.ref24)))
+                tgt24_corr = eval.apply_pipeline_to_patches(tgt24, self.ref24)
+                dE_final = float(np.mean(eval.compute_deltaE_00(tgt24_corr, self.ref24)))
+                
+                image = calib.apply_pipeline(image, self.ref24, tgt24)
+            except Exception as e:
+                print(f"Calibration skipped for {source_name}: {e}")
         
-        rind_mask = np.zeros((h, w), dtype=np.uint8)
-        flesh_l = np.zeros((h, w), dtype=np.uint8)
-        flesh_r = np.zeros((h, w), dtype=np.uint8)
+        # Fallback to pure pixels if checker not found
+        if mm_per_px is None:
+            mm_per_px = 1.0 / scale_ratio 
+
+        # --- YOLO INFERENCE ---
+        results = self.model(image, conf=0.25, verbose=False)
+        rind_mask, flesh_mask = np.zeros((h, w), dtype=np.uint8), np.zeros((h, w), dtype=np.uint8)
 
         if results[0].masks is None:
             return ProcessResult(success=False, message="No masks detected.")
 
-        # --- THE FIX: Load 0 (Whole), 1 (Left), and 2 (Right) ---
         for mask_data, cls in zip(results[0].masks.xy, results[0].boxes.cls):
             contour = np.array(mask_data, dtype=np.int32)
-            c_id = int(cls)
-            if c_id == 0:
-                cv2.drawContours(rind_mask, [contour], -1, 255, -1)
-            elif c_id == 1:
-                cv2.drawContours(flesh_l, [contour], -1, 255, -1)
-            elif c_id == 2:
-                cv2.drawContours(flesh_r, [contour], -1, 255, -1)
-
-        flesh_combined = cv2.bitwise_or(flesh_l, flesh_r)
+            if int(cls) == 0: cv2.drawContours(rind_mask, [contour], -1, 255, -1)
+            elif int(cls) == 1: cv2.drawContours(flesh_mask,[contour], -1, 255, -1)
 
-        perimeter_data = self.get_stable_perimeter_data(rind_mask, flesh_combined)
+        perimeter_data = self.get_stable_perimeter_data(rind_mask, flesh_mask)
         if perimeter_data is None: return ProcessResult(success=False, message="No stable perimeter.")
 
         t_data, r_raw, (cx, cy), rind_cnt = perimeter_data
         scale = np.mean(r_raw)
-        if scale <= 0: return ProcessResult(success=False, message="Invalid perimeter scale.")
         
         try:
             popt, _ = curve_fit(
@@ -190,48 +213,48 @@ class WatermelonProcessor:
             )
         except Exception as exc: return ProcessResult(success=False, message=f"Fit failed: {exc}")
 
-        denom = np.sum((r_raw / scale - 1) ** 2)
-        if denom == 0: return ProcessResult(success=False, message="R2 denominator became zero.")
-
-        r2 = 1 - (np.sum((r_raw / scale - self.watermelon_model(t_data, *popt)) ** 2) / denom)
+        r2 = 1 - (np.sum((r_raw / scale - self.watermelon_model(t_data, *popt)) ** 2) / np.sum((r_raw / scale - 1) ** 2))
         t_fit = np.linspace(-np.pi, np.pi, 500)
         r_fit = self.watermelon_model(t_fit, *popt) * scale
         fit_pts = np.array([[r * np.cos(t) + cx, cy - r * np.sin(t)] for t, r in zip(t_fit, r_fit)])
         
-        orig_scale = 1.0 / scale_ratio
+        # --- FEATURE EXTRACTION (IN MM) ---
         width_px = float(np.max(fit_pts[:, 0]) - np.min(fit_pts[:, 0]))
         height_px = float(np.max(fit_pts[:, 1]) - np.min(fit_pts[:, 1]))
         diffs = np.diff(fit_pts, axis=0)
         perimeter_px = float(np.sum(np.linalg.norm(diffs, axis=1)) + np.linalg.norm(fit_pts[-1] - fit_pts[0]))
 
-        width_val = width_px * PIXELS_TO_CM * orig_scale
-        height_val = height_px * PIXELS_TO_CM * orig_scale
-        perimeter_val = perimeter_px * PIXELS_TO_CM * orig_scale
+        width_val = width_px * mm_per_px
+        height_val = height_px * mm_per_px
+        perimeter_val = perimeter_px * mm_per_px
 
-        # --- THE FIX: Dual-Mask Midline ---
-        midline = self.get_dual_mask_midline(flesh_l, flesh_r, rind_cnt, fit_pts, cx, cy)
-        
-        output = blend_mask_overlays(image, rind_mask, flesh_combined)
-        if len(midline) > 1:
-            cv2.polylines(output, [midline.astype(np.int32)], False, (0, 255, 255), 3)
+        # --- DRAWING ---
+        midline = self.get_ray_scan_midline(flesh_mask, rind_cnt, fit_pts, cx, cy)
+        output = blend_mask_overlays(image, rind_mask, flesh_mask)
+        if len(midline) > 1: cv2.polylines(output, [midline.astype(np.int32)], False, (0, 255, 255), 3)
         cv2.polylines(output, [fit_pts.astype(np.int32)], True, (0, 255, 0), 3)
 
+        # Draw Color Checker Box
+        if checker_corners is not None:
+            cv2.polylines(output,[np.int32(checker_corners)], True, (0, 165, 255), 4)
+
         stem = stem_tip_tangent_deg(rind_cnt, (cx, cy))
         if stem is not None:
             tx, ty, tdeg = stem
-            rad = np.deg2rad(tdeg)
             L = min(w, h) * 0.08
+            rad = np.deg2rad(tdeg)
             p1 = (int(round(tx)), int(round(ty)))
             p2 = (int(round(tx + L * np.cos(rad))), int(round(ty + L * np.sin(rad))))
             cv2.circle(output, p1, 6, (255, 0, 255), -1)
             cv2.line(output, p1, p2, (255, 0, 255), 2)
 
-        _, buffer = cv2.imencode('.jpg', output, [cv2.IMWRITE_JPEG_QUALITY, 85])
+        _, buffer = cv2.imencode('.jpg', output,[cv2.IMWRITE_JPEG_QUALITY, 85])
         img_base64 = base64.b64encode(buffer).decode('utf-8')
 
         return ProcessResult(
             success=True, message="Success", r2_score=float(r2),
             width_val=width_val, height_val=height_val, perimeter_val=perimeter_val,
+            delta_e_initial=dE_initial, delta_e_final=dE_final,
             image_base64=img_base64, filename=source_name
         )
 
@@ -270,7 +293,4 @@ async def process_single(file: UploadFile = File(...)):
     del img, nparr, contents
     gc.collect()
 
-    return res.__dict__
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    return res.__dict__