File size: 5,823 Bytes
dff2bf7 2d2c726 dff2bf7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
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 supervision as sv
# ========== 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 . food . drink . bottle" # Customize sesuai produk kompetitor
# 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
if not is_overlap(dinox_box, nestle_boxes):
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 Product Nestle: {total_nestle}\n\n"
result_text += "Competitor Products\n\n"
if competitor_class_count:
for class_name, count in competitor_class_count.items():
result_text += f"{class_name}: {count}\n"
else:
result_text += "No competitors detected\n"
result_text += f"\nTotal Competitor: {total_competitor}"
# ========== [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)
for comp in competitor_boxes:
x1, y1, x2, y2 = comp['box']
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 2)
cv2.putText(img, f"{comp['class']} {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
# ========== Gradio Interface ==========
with gr.Blocks() as iface:
with gr.Row():
input_image = gr.Image(type="pil", label="Input Image")
output_image = gr.Image(label="Detection Result")
output_text = gr.Textbox(label="Product Counts")
detect_button = gr.Button("Detect Products")
detect_button.click(
fn=detect_combined,
inputs=input_image,
outputs=[output_image, output_text]
)
iface.launch() |