Delete app-error.py

app-error.py

@@ -1,275 +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
-from dds_cloudapi_sdk import Config, Client
-from dds_cloudapi_sdk.tasks.dinox import DinoxTask
-from dds_cloudapi_sdk.tasks.types import DetectionTarget
-from dds_cloudapi_sdk import TextPrompt
-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"))
-
-# DINO-X Config
-DINOX_API_KEY = os.getenv("DINO_X_API_KEY")
-DINOX_PROMPT = "beverage . bottle . cans . boxed milk . milk"  # Customize sesuai produk kompetitor : food . drink
-
-# Inisialisasi Model
-rf = Roboflow(api_key=rf_api_key)
-project = rf.workspace(workspace).project(project_name)
-yolo_model = project.version(model_version).model
-
-dinox_config = Config(DINOX_API_KEY)
-dinox_client = Client(dinox_config)
-
-# ========== Fungsi Deteksi Kombinasi ========== 
-def detect_combined(image):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        image.save(temp_file, format="JPEG")
-        temp_path = temp_file.name
-
-    try:
-        # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ========== 
-        yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
-
-        # Hitung per class Nestlé
-        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] DINO-X: Deteksi Kompetitor ========== 
-        image_url = dinox_client.upload_file(temp_path)
-        task = DinoxTask(
-            image_url=image_url,
-            prompts=[TextPrompt(text=DINOX_PROMPT)],
-            bbox_threshold=0.25,
-            targets=[DetectionTarget.BBox]
-        )
-        dinox_client.run_task(task)
-        dinox_pred = task.result.objects
-
-        # Filter & Hitung Kompetitor
-        competitor_class_count = {}
-        competitor_boxes = []
-        for obj in dinox_pred:
-            dinox_box = obj.bbox
-            # Filter objek yang sudah terdeteksi oleh YOLO (Overlap detection)
-            if not is_overlap(dinox_box, nestle_boxes):  # Ignore if overlap with YOLO detections
-                class_name = obj.category.strip().lower()  # Normalisasi nama kelas
-                competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
-                competitor_boxes.append({
-                    "class": class_name,
-                    "box": dinox_box,
-                    "confidence": obj.score
-                })
-
-        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"
-
-        #result_text += "Competitor Products\n\n"
-        if competitor_class_count:
-            result_text += f"Total Unclassified Products: {total_competitor}\n"  # Hanya total, tidak per kelas
-        else:
-            result_text += "No Unclassified Products detected\n"
-
-        # ========== [4] Visualisasi ========== 
-        img = cv2.imread(temp_path)
-
-        # 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)
-
-        # Kompetitor (Merah) dengan nama 'unclassified'
-        for comp in competitor_boxes:
-            x1, y1, x2, y2 = comp['box']
-            
-            # Define a list of target classes to rename
-            unclassified_classes = ["beverage", "cans", "bottle", "boxed milk", "milk"]
-            
-            # Normalize the class name to be case-insensitive and check if it's in the unclassified list
-            display_name = "unclassified" if any(class_name in comp['class'].lower() for class_name 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 deteksi overlap bounding box
-    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
-
-        # Hitung area overlap
-        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()
-    frame_count = 0
-    previous_detections = {}  # Untuk menyimpan deteksi objek dari frame sebelumnya
-
-    try:
-        # Convert video to MP4 if necessary
-        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}"
-
-        # Read video and process frames
-        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 for 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
-
-            # Save frame temporarily for predictions
-            frame_path = os.path.join(temp_frames_dir, f"frame_{frame_count}.jpg")
-            cv2.imwrite(frame_path, frame)
-
-            # Process predictions for the current frame
-            predictions = yolo_model.predict(frame_path, confidence=50, overlap=80).json()
-
-            # Track current frame detections
-            current_detections = {}
-            for prediction in predictions['predictions']:
-                class_name = prediction['class']
-                x, y, w, h = prediction['x'], prediction['y'], prediction['width'], prediction['height']
-                # Generate a unique ID for each detection (can use coordinates or other method)
-                object_id = f"{class_name}_{x}_{y}"
-
-                if object_id not in current_detections:
-                    current_detections[object_id] = class_name
-
-                # Draw bounding box for detected objects
-                cv2.rectangle(frame, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), (0,255,0), 2)
-                cv2.putText(frame, class_name, (int(x-w/2), int(y-h/2-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
-
-            # Calculate the changes from previous detections
-            removed_objects = set(previous_detections.keys()) - set(current_detections.keys())
-            new_objects = set(current_detections.keys()) - set(previous_detections.keys())
-
-            # Update counts for objects
-            object_counts = {}
-            for detection_id in current_detections.keys():
-                class_name = current_detections[detection_id]
-                object_counts[class_name] = object_counts.get(class_name, 0) + 1
-
-            # Update object counts based on removed objects
-            for detection_id in removed_objects:
-                class_name = previous_detections[detection_id]
-                if class_name in object_counts:
-                    object_counts[class_name] -= 1
-                    if object_counts[class_name] <= 0:
-                        del object_counts[class_name]
-
-            # Generate display text for counts
-            count_text = ""
-            total_product_count = 0
-            for class_name, count in object_counts.items():
-                count_text += f"{class_name}: {count}\n"
-                total_product_count += count
-            count_text += f"\nTotal Product: {total_product_count}"
-
-            # Overlay the counts text onto the frame
-            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  # Move down for next line
-
-            # Write processed frame to output video
-            output_video.write(frame)
-            frame_count += 1
-
-            # Update previous_detections for the next frame
-            previous_detections = current_detections
-
-        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")
-            detect_image_button = gr.Button("Detect Image")
-            output_image = gr.Image(label="Detect Object")
-            output_text = gr.Textbox(label="Counting Object")
-            detect_image_button.click(fn=detect_combined, inputs=input_image, outputs=[output_image, output_text])
-
-        with gr.Column():
-            input_video = gr.Video(label="Input Video")
-            detect_video_button = gr.Button("Detect Video")
-            output_video = gr.Video(label="Output Video")
-            detect_video_button.click(fn=detect_objects_in_video, inputs=input_video, outputs=[output_video])
-
-iface.launch()