Update app.py

app.py

@@ -17,205 +17,123 @@ workspace = os.getenv("ROBOFLOW_WORKSPACE")
 project_name = os.getenv("ROBOFLOW_PROJECT")
 model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
 
-# CountGD Config (Replace DINO-X)
-# Set your CountGD API key in your .env file (e.g., COUNTGD_API_KEY=YourEncodedAPIKey)
+# CountGD Config
 COUNTGD_API_KEY = os.getenv("COUNTGD_API_KEY")
 
-# Inisialisasi YOLO Model from Roboflow
+# Inisialisasi YOLO Model dari Roboflow
 rf = Roboflow(api_key=rf_api_key)
 project = rf.workspace(workspace).project(project_name)
 yolo_model = project.version(model_version).model
 
-# ========== Function to Check Overlap ==========
-def is_overlap(box1, boxes2, threshold=0.3):
-    """
-    Checks if box1 (format: (x_min, y_min, x_max, y_max)) overlaps with any boxes in boxes2.
-    boxes2 is a list of YOLO bounding boxes in the format (x_center, y_center, width, height).
-    Returns True if the overlap ratio of box1 is greater than the threshold.
-    """
-    x1_min, y1_min, x1_max, y1_max = box1
-    for b2 in boxes2:
-        x_center, y_center, w2, h2 = b2
-        x2_min = x_center - w2 / 2
-        x2_max = x_center + w2 / 2
-        y2_min = y_center - h2 / 2
-        y2_max = y_center + h2 / 2
-
-        # Calculate overlap area
-        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_box1 > 0 and (area_overlap / area_box1) > threshold:
-                return True
-    return False
-
-# ========== Combined Object Detection Function ==========
+# ========== Fungsi untuk Menghitung IoU ==========
+def iou(boxA, boxB):
+    xA = max(boxA[0], boxB[0])
+    yA = max(boxA[1], boxB[1])
+    xB = min(boxA[2], boxB[2])
+    yB = min(boxA[3], boxB[3])
+
+    interArea = max(0, xB - xA) * max(0, yB - yA)
+    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
+    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
+
+    return interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
+
+# ========== 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:
-        # ===== YOLO Detection (Nestlé products) =====
+        # YOLO Detection (Produk Nestlé)
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
         nestle_class_count = {}
-        nestle_boxes = []  # List to hold YOLO bounding boxes (format: x_center, y_center, width, height)
+        nestle_boxes = []  # (x_center, y_center, width, height)
         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())
-        
-        # ===== CountGD Detection (Competitor products) =====
+
+        # CountGD Detection (Produk Kompetitor)
         url = "https://api.landing.ai/v1/tools/text-to-object-detection"
-        files = {"image": open(temp_path, "rb")}
-        data = {"prompts": ["mixed box"], "model": "countgd"}
-        headers = {"Authorization": f"Basic {COUNTGD_API_KEY}"}
-        response = requests.post(url, files=files, data=data, headers=headers)
-        result = response.json()
-        
         competitor_class_count = {}
-        competitor_boxes = []  # List to hold CountGD bounding boxes (format: x_min, y_min, x_max, y_max)
-        if 'data' in result:
-            for obj in result['data'][0]:
-                if 'bounding_box' in obj:
-                    # CountGD returns bounding_box as [x_min, y_min, x_max, y_max]
-                    x1, y1, x2, y2 = obj['bounding_box']
-                    countgd_box = (x1, y1, x2, y2)
-                    # Only add CountGD detection if it does NOT significantly overlap with any YOLO detection
-                    if not is_overlap(countgd_box, nestle_boxes, threshold=0.3):
-                        class_name = "unclassified"  # Generic label for competitor objects
-                        competitor_class_count[class_name] = competitor_class_count.get(class_name, 0) + 1
+        competitor_boxes = []
+        COUNTGD_PROMPTS = ["cans", "bottle", "mixed box"]
+        headers = {"Authorization": f"Basic {COUNTGD_API_KEY}"}
+
+        for prompt in COUNTGD_PROMPTS:
+            with open(temp_path, "rb") as f:
+                files = {"image": f}
+                data = {"prompts": [prompt], "model": "countgd"}
+                response = requests.post(url, files=files, data=data, headers=headers)
+            result = response.json()
+
+            if 'data' in result and result['data']:
+                detections = result['data'][0]
+                detections_sorted = sorted(detections, key=lambda obj: obj.get('confidence', 0), reverse=True)
+
+                for obj in detections_sorted:
+                    if 'bounding_box' in obj:
+                        x1, y1, x2, y2 = obj['bounding_box']
+                        countgd_box = (x1, y1, x2, y2)
+
+                        # Hapus duplikasi dengan deteksi YOLO
+                        if any(iou(countgd_box, yolo_box) > 0.3 for yolo_box in nestle_boxes):
+                            continue
+
+                        # Hapus duplikasi antar deteksi CountGD
+                        if any(iou(countgd_box, existing_box) > 0.3 for existing_box in competitor_boxes):
+                            continue
+
+                        label = obj.get('label', prompt)
+                        
+                        # Hapus "mixed box" jika ada "cans" atau "bottle" yang lebih spesifik
+                        if label == "mixed box" and ("cans" in competitor_class_count or "bottle" in competitor_class_count):
+                            continue
+
+                        competitor_class_count[label] = competitor_class_count.get(label, 0) + 1
                         competitor_boxes.append(countgd_box)
+
         total_competitor = sum(competitor_class_count.values())
-        
-        # ===== Format Output Text =====
+
+        # Format Output Text
         result_text = "Product Nestlé\n\n"
         for class_name, count in nestle_class_count.items():
             result_text += f"{class_name}: {count}\n"
         result_text += f"\nTotal Products Nestlé: {total_nestle}\n\n"
+        
         if total_competitor:
-            result_text += f"Total Unclassified Products: {total_competitor}\n"
+            result_text += f"\nTotal Unclassified Products: {total_competitor}\n"
         else:
             result_text += "No Unclassified Products detected\n"
-        
-        # ===== Visualization =====
+
+        # Visualisasi Bounding Box
         img = cv2.imread(temp_path)
-        # Draw YOLO boxes in green
         for pred in yolo_pred['predictions']:
             x, y, w, h = pred['x'], pred['y'], pred['width'], pred['height']
             pt1 = (int(x - w/2), int(y - h/2))
             pt2 = (int(x + w/2), int(y + h/2))
             cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
-            cv2.putText(img, pred['class'], (pt1[0], pt1[1]-10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
-        # Draw CountGD boxes in red
+            cv2.putText(img, pred['class'], (pt1[0], pt1[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,0), 3)
+
         for box in competitor_boxes:
             x1, y1, x2, y2 = box
             cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
-            cv2.putText(img, "unclassified", (int(x1), int(y1)-10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
-        
+            cv2.putText(img, "unclassified", (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
+
         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:
         if os.path.exists(temp_path):
             os.remove(temp_path)
 
-# ========== Video Detection Functions ==========
-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 = {}  # For storing previous frame's detections
-
-    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}"
-
-        # Open video for processing
-        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)
-
-        # Setup VideoWriter for output
-        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 for YOLO detection
-            frame_path = os.path.join(temp_frames_dir, f"frame_{frame_count}.jpg")
-            cv2.imwrite(frame_path, frame)
-
-            # YOLO detection on the frame
-            predictions = yolo_model.predict(frame_path, confidence=50, overlap=80).json()
-
-            # Draw YOLO detections on the frame
-            current_detections = {}
-            for prediction in predictions['predictions']:
-                class_name = prediction['class']
-                x, y, w, h = prediction['x'], prediction['y'], prediction['width'], prediction['height']
-                object_id = f"{class_name}_{x}_{y}_{w}_{h}"
-                if object_id not in current_detections:
-                    current_detections[object_id] = class_name
-                pt1 = (int(x - w/2), int(y - h/2))
-                pt2 = (int(x + w/2), int(y + h/2))
-                cv2.rectangle(frame, pt1, pt2, (0,255,0), 2)
-                cv2.putText(frame, class_name, (pt1[0], pt1[1]-10),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
-
-            # Count objects and overlay text
-            object_counts = {}
-            for detection_id in current_detections:
-                cls = current_detections[detection_id]
-                object_counts[cls] = object_counts.get(cls, 0) + 1
-
-            count_text = ""
-            total_product_count = 0
-            for cls, count in object_counts.items():
-                count_text += f"{cls}: {count}\n"
-                total_product_count += count
-            count_text += f"\nTotal Product: {total_product_count}"
-            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
-            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>""")
@@ -227,10 +145,5 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", ne
             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()