File size: 1,553 Bytes
192052a d98d85d 9289a22 d98d85d 9289a22 d98d85d 9289a22 05b0eb3 d98d85d 05b0eb3 d98d85d 05b0eb3 9289a22 05b0eb3 9289a22 05b0eb3 9289a22 d98d85d 9289a22 | 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 | from fastapi import FastAPI, UploadFile, File
import cv2
import numpy as np
import uvicorn
app = FastAPI()
def get_distance(bg_img, target_img):
# 1. Preprocessing: Ubah ke Gray dan Blur untuk ngilangin noise
bg_gray = cv2.cvtColor(bg_img, cv2.COLOR_BGR2GRAY)
target_gray = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)
# 2. Canny Edge Detection: Ini kuncinya! Kita cari garis tepi
bg_edge = cv2.Canny(bg_gray, 100, 200)
target_edge = cv2.Canny(target_gray, 100, 200)
# 3. Template Matching pada hasil Edge Detection
res = cv2.matchTemplate(bg_edge, target_edge, cv2.TM_CCOEFF_NORMED)
_, _, _, max_loc = cv2.minMaxLoc(res)
# max_loc[0] adalah koordinat X terbaik
return max_loc[0]
@app.post("/solve")
async def solve(background: UploadFile = File(...), target: UploadFile = File(...)):
try:
bg_bytes = await background.read()
target_bytes = await target.read()
bg_img = cv2.imdecode(np.frombuffer(bg_bytes, np.uint8), cv2.IMREAD_COLOR)
target_img = cv2.imdecode(np.frombuffer(target_bytes, np.uint8), cv2.IMREAD_COLOR)
# Eksekusi pencarian jarak
distance = get_distance(bg_img, target_img)
return {
"status": "success",
"distance": int(distance),
"info": "Edge detection applied for maximum accuracy"
}
except Exception as e:
return {"status": "error", "message": str(e)}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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