Delete app.py

app.py

@@ -1,282 +0,0 @@
-import gradio as gr
-from dotenv import load_dotenv
-from roboflow import Roboflow
-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 ==========
-from PIL import Image
-
-# Fungsi untuk deteksi dengan resize
-from PIL import Image
-
-# 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:
-        # Simpan dimensi asli untuk scaling
-        original_width, original_height = image.size
-
-        # 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
-        }
-        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]
-                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']
-            x1 = int(x - w/2)
-            y1 = int(y - h/2)
-            x2 = int(x + w/2)
-            y2 = int(y + h/2)
-
-            # Scale bounding box to original size
-            scale_x = original_width / 640
-            scale_y = original_height / 640
-            x1_original = int(x1 * scale_x)
-            y1_original = int(y1 * scale_y)
-            x2_original = int(x2 * scale_x)
-            y2_original = int(y2 * scale_y)
-
-            cv2.rectangle(img, (x1_original, y1_original), (x2_original, y2_original), (0, 255, 0), 2)
-            cv2.putText(img, pred['class'], (x1_original, y1_original - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-        # Gambar bounding box untuk kompetitor (Merah)
-        for comp in competitor_boxes:
-            x1, y1, x2, y2 = comp['box']
-            # Scale bounding box to original size
-            x1_original = int(x1 * scale_x)
-            y1_original = int(y1 * scale_y)
-            x2_original = int(x2 * scale_x)
-            y2_original = int(y2 * scale_y)
-
-            cv2.rectangle(img, (x1_original, y1_original), (x2_original, y2_original), (0, 0, 255), 2)
-            cv2.putText(img, f"{comp['class']} {comp['confidence']:.2f}", (x1_original, y1_original - 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("""<div style="text-align: center;"><h1>NESTLE - STOCK COUNTING</h1></div>""")
-    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()