Create app.py

app.py

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+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"  # 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=60, 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"]
+            
+            # 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()
+    all_class_count = {}  # To store cumulative counts for all frames
+    nestle_total = 0  # Total Nestlé count
+    frame_count = 0
+
+    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 frame
+            predictions = yolo_model.predict(frame_path, confidence=60, overlap=80).json()
+
+            # Update class count for this frame
+            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 cumulative count for all frames
+            for class_name, count in frame_class_count.items():
+                all_class_count[class_name] = all_class_count.get(class_name, 0) + count
+
+            # Update total Nestlé products count
+            nestle_total = sum(all_class_count.values())
+
+            # Create a vertical layout for counts (dynamically updated)
+            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}"
+
+            # 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
+
+        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_objects, 
+        inputs=input_image, 
+        outputs=[output_image, output_text]
+    )
+
+#    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()