import gradio as gr from dotenv import load_dotenv from roboflow import Roboflow from PIL import Image import tempfile import os import requests import cv2 import numpy as np import subprocess # ========== Konfigurasi ========== load_dotenv() # Roboflow Config rf_api_key = os.getenv("ROBOFLOW_API_KEY") workspace = os.getenv("ROBOFLOW_WORKSPACE") project_name = os.getenv("ROBOFLOW_PROJECT") model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION")) # OWLv2 Config OWLV2_API_KEY = os.getenv("COUNTGD_API_KEY") OWLV2_PROMPTS = ["bottle", "tetra pak","cans", "carton drink"] # Inisialisasi Model YOLO rf = Roboflow(api_key=rf_api_key) project = rf.workspace(workspace).project(project_name) yolo_model = project.version(model_version).model # ========== Fungsi Deteksi Kombinasi ========== # Fungsi untuk deteksi dengan resize def detect_combined(image): # Simpan gambar input ke file sementara with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: image.save(temp_file, format="JPEG") temp_path = temp_file.name try: # Resize gambar input menjadi 640x640 img = Image.open(temp_path) img = img.resize((640, 640), Image.Resampling.LANCZOS) # Ganti ANTIALIAS dengan LANCZOS img.save(temp_path, format="JPEG") # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ========== yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json() # Hitung per class Nestlé dan simpan bounding box (format: (x_center, y_center, width, height)) nestle_class_count = {} nestle_boxes = [] for pred in yolo_pred['predictions']: class_name = pred['class'] nestle_class_count[class_name] = nestle_class_count.get(class_name, 0) + 1 nestle_boxes.append((pred['x'], pred['y'], pred['width'], pred['height'])) total_nestle = sum(nestle_class_count.values()) # ========== [2] OWLv2: Deteksi Kompetitor ========== headers = { "Authorization": "Basic " + OWLV2_API_KEY, } data = { "prompts": OWLV2_PROMPTS, "model": "owlv2", "confidence": 0.25 # Set confidence threshold to 0.25 } with open(temp_path, "rb") as f: files = {"image": f} response = requests.post("https://api.landing.ai/v1/tools/text-to-object-detection", files=files, data=data, headers=headers) result = response.json() owlv2_objects = result['data'][0] if 'data' in result else [] competitor_class_count = {} competitor_boxes = [] for obj in owlv2_objects: if 'bounding_box' in obj: bbox = obj['bounding_box'] # Format: [x1, y1, x2, y2] # Filter objek yang sudah terdeteksi oleh YOLO (Overlap detection) if not is_overlap(bbox, nestle_boxes): class_name = obj.get('label', 'unknown').strip().lower() competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1 competitor_boxes.append({ "class": class_name, "box": bbox, "confidence": obj.get("score", 0) }) total_competitor = sum(competitor_class_count.values()) # ========== [3] Format Output ========== result_text = "Product Nestle\n\n" for class_name, count in nestle_class_count.items(): result_text += f"{class_name}: {count}\n" result_text += f"\nTotal Products Nestle: {total_nestle}\n\n" if competitor_class_count: result_text += f"Total Unclassified Products: {total_competitor}\n" else: result_text += "No Unclassified Products detected\n" # ========== [4] Visualisasi ========== img = cv2.imread(temp_path) # Gambar bounding box untuk produk Nestlé (Hijau) for pred in yolo_pred['predictions']: x, y, w, h = pred['x'], pred['y'], pred['width'], pred['height'] cv2.rectangle(img, (int(x - w/2), int(y - h/2)), (int(x + w/2), int(y + h/2)), (0, 255, 0), 2) cv2.putText(img, pred['class'], (int(x - w/2), int(y - h/2 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # Gambar bounding box untuk kompetitor (Merah) dengan label 'unclassified' jika sesuai for comp in competitor_boxes: x1, y1, x2, y2 = comp['box'] unclassified_classes = ["cans"] display_name = "unclassified" if any(cls in comp['class'].lower() for cls in unclassified_classes) else comp['class'] cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2) cv2.putText(img, f"{display_name} {comp['confidence']:.2f}", (int(x1), int(y1 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) output_path = "/tmp/combined_output.jpg" cv2.imwrite(output_path, img) return output_path, result_text except Exception as e: return temp_path, f"Error: {str(e)}" finally: os.remove(temp_path) def is_overlap(box1, boxes2, threshold=0.3): """ Fungsi untuk mendeteksi overlap bounding box. Parameter: - box1: Bounding box pertama dengan format (x1, y1, x2, y2) - boxes2: List bounding box lainnya dengan format (x_center, y_center, width, height) """ x1_min, y1_min, x1_max, y1_max = box1 for b2 in boxes2: x2, y2, w2, h2 = b2 x2_min = x2 - w2/2 x2_max = x2 + w2/2 y2_min = y2 - h2/2 y2_max = y2 + h2/2 dx = min(x1_max, x2_max) - max(x1_min, x2_min) dy = min(y1_max, y2_max) - max(y1_min, y2_min) if (dx >= 0) and (dy >= 0): area_overlap = dx * dy area_box1 = (x1_max - x1_min) * (y1_max - y1_min) if area_overlap / area_box1 > threshold: return True return False # ========== Fungsi untuk Deteksi Video ========== def convert_video_to_mp4(input_path, output_path): try: subprocess.run(['ffmpeg', '-i', input_path, '-vcodec', 'libx264', '-acodec', 'aac', output_path], check=True) return output_path except subprocess.CalledProcessError as e: return None, f"Error converting video: {e}" def detect_objects_in_video(video_path): temp_output_path = "/tmp/output_video.mp4" temp_frames_dir = tempfile.mkdtemp() all_class_count = {} # Untuk menyimpan total hitungan objek dari semua frame nestle_total = 0 frame_count = 0 try: # Convert video ke MP4 jika perlu if not video_path.endswith(".mp4"): video_path, err = convert_video_to_mp4(video_path, temp_output_path) if not video_path: return None, f"Video conversion error: {err}" # Membaca dan memproses frame video video = cv2.VideoCapture(video_path) frame_rate = int(video.get(cv2.CAP_PROP_FPS)) frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_size = (frame_width, frame_height) # VideoWriter untuk output video fourcc = cv2.VideoWriter_fourcc(*'mp4v') output_video = cv2.VideoWriter(temp_output_path, fourcc, frame_rate, frame_size) while True: ret, frame = video.read() if not ret: break # Simpan frame untuk prediksi frame_path = os.path.join(temp_frames_dir, f"frame_{frame_count}.jpg") cv2.imwrite(frame_path, frame) # Proses prediksi untuk frame predictions = yolo_model.predict(frame_path, confidence=60, overlap=80).json() # Update hitungan objek untuk frame ini frame_class_count = {} for prediction in predictions['predictions']: class_name = prediction['class'] frame_class_count[class_name] = frame_class_count.get(class_name, 0) + 1 cv2.rectangle(frame, (int(prediction['x'] - prediction['width']/2), int(prediction['y'] - prediction['height']/2)), (int(prediction['x'] + prediction['width']/2), int(prediction['y'] + prediction['height']/2)), (0, 255, 0), 2) cv2.putText(frame, class_name, (int(prediction['x'] - prediction['width']/2), int(prediction['y'] - prediction['height']/2 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # Update hitungan kumulatif for class_name, count in frame_class_count.items(): all_class_count[class_name] = all_class_count.get(class_name, 0) + count nestle_total = sum(all_class_count.values()) # Overlay teks hitungan pada frame count_text = "Cumulative Object Counts\n" for class_name, count in all_class_count.items(): count_text += f"{class_name}: {count}\n" count_text += f"\nTotal Product Nestlé: {nestle_total}" y_offset = 20 for line in count_text.split("\n"): cv2.putText(frame, line, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) y_offset += 30 output_video.write(frame) frame_count += 1 video.release() output_video.release() return temp_output_path except Exception as e: return None, f"An error occurred: {e}" # ========== Gradio Interface ========== with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", neutral_hue="slate")) as iface: gr.Markdown("""

NESTLE - STOCK COUNTING

""") with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Input Image") with gr.Column(): output_image = gr.Image(label="Detect Object") with gr.Column(): output_text = gr.Textbox(label="Counting Object") # Tombol untuk memproses input detect_button = gr.Button("Detect") # Hubungkan tombol dengan fungsi deteksi detect_button.click( fn=detect_combined, inputs=input_image, outputs=[output_image, output_text] ) iface.launch()