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import traceback
import numpy as np
import cv2
from flask import request, jsonify
# Import app, models, and logic functions
from pipeline import app, models, logic
@app.route('/process', methods=['POST'])
def process_item():
print("\n" + "="*50)
print("➡ [Request] Received new request to /process")
try:
data = request.get_json()
if not data: return jsonify({"error": "Invalid JSON payload"}), 400
object_name = data.get('objectName')
description = data.get('objectDescription')
image_url = data.get('objectImage')
if not all([object_name, description]):
return jsonify({"error": "objectName and objectDescription are required."}), 400
canonical_label = logic.get_canonical_label(object_name)
text_embedding = logic.get_text_embedding(description, models)
response_data = {
"canonicalLabel": canonical_label,
"text_embedding": text_embedding,
}
if image_url:
print("--- Image URL provided, processing visual features... ---")
image = logic.download_image_from_url(image_url)
object_crop = logic.detect_and_crop(image, canonical_label, models)
visual_features = logic.extract_features(object_crop)
response_data.update(visual_features)
else:
print("--- No image URL provided, skipping visual feature extraction. ---")
print("✅ Successfully processed item.")
print("="*50)
return jsonify(response_data), 200
except Exception as e:
print(f"❌ Error in /process: {e}")
traceback.print_exc()
return jsonify({"error": str(e)}), 500
@app.route('/compare', methods=['POST'])
def compare_items():
print("\n" + "="*50)
print("➡ [Request] Received new request to /compare")
try:
data = request.get_json()
if not data: return jsonify({"error": "Invalid JSON payload"}), 400
query_item = data.get('queryItem')
search_list = data.get('searchList')
if not all([query_item, search_list]):
return jsonify({"error": "queryItem and searchList are required."}), 400
query_text_emb = np.array(query_item['text_embedding'])
results = []
print(f"--- Comparing 1 query item against {len(search_list)} items ---")
for item in search_list:
item_id = item.get('_id')
print(f"\n [Checking] Item ID: {item_id}")
try:
text_emb_found = np.array(item['text_embedding'])
text_score = logic.cosine_similarity(query_text_emb, text_emb_found)
print(f" - Text Score: {text_score:.4f}")
has_query_image = 'shape_features' in query_item and query_item['shape_features']
has_item_image = 'shape_features' in item and item['shape_features']
if has_query_image and has_item_image:
print(" - Both items have images. Performing visual comparison.")
from pipeline import FEATURE_WEIGHTS # Import constant
query_shape = np.array(query_item['shape_features'])
query_color = np.array(query_item['color_features']).astype("float32")
query_texture = np.array(query_item['texture_features']).astype("float32")
found_shape = np.array(item['shape_features'])
found_color = np.array(item['color_features']).astype("float32")
found_texture = np.array(item['texture_features']).astype("float32")
shape_dist = cv2.matchShapes(query_shape, found_shape, cv2.CONTOURS_MATCH_I1, 0.0)
shape_score = 1.0 / (1.0 + shape_dist)
color_score = cv2.compareHist(query_color, found_color, cv2.HISTCMP_CORREL)
texture_score = cv2.compareHist(query_texture, found_texture, cv2.HISTCMP_CORREL)
raw_image_score = (FEATURE_WEIGHTS["shape"] * shape_score +
FEATURE_WEIGHTS["color"] * color_score +
FEATURE_WEIGHTS["texture"] * texture_score)
print(f"Raw Image Score: {raw_image_score:.4f}")
image_score = logic.stretch_image_score(raw_image_score)
final_score = 0.4 * image_score + 0.6 * text_score
print(f" - Image Score: {image_score:.4f} | Final Score: {final_score:.4f}")
else:
print(" - One or both items missing image. Using text score only.")
final_score = text_score
from pipeline import FINAL_SCORE_THRESHOLD # Import constant
if final_score >= FINAL_SCORE_THRESHOLD:
print(f" - ✅ ACCEPTED (Score >= {FINAL_SCORE_THRESHOLD})")
results.append({
"_id": item_id,
"score": round(final_score, 4),
"objectName": item.get("objectName"),
"objectDescription": item.get("objectDescription"),
"objectImage": item.get("objectImage"),
})
else:
print(f" - ❌ REJECTED (Score < {FINAL_SCORE_THRESHOLD})")
except Exception as e:
print(f" [Skipping] Item {item_id} due to processing error: {e}")
continue
results.sort(key=lambda x: x["score"], reverse=True)
print(f"\n✅ Search complete. Found {len(results)} potential matches.")
print("="*50)
return jsonify({"matches": results}), 200
except Exception as e:
print(f"❌ Error in /compare: {e}")
traceback.print_exc()
return jsonify({"error": str(e)}), 500 |