Spaces:
Sleeping
Sleeping
File size: 1,839 Bytes
9701ff3 |
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 |
import requests
import matplotlib.pyplot as plt
from PIL import Image
import csv
import os
API_URL = "https://api-inference.huggingface.co/models/OttoYu/Tree-Inspection"
headers = {"Authorization": "Bearer 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"}
def TreeAI(image_path):
def query(filename):
with open(filename, "rb") as f:
data = f.read()
response = requests.post(API_URL, headers=headers, data=data)
return response.json()
output = query(image_path)
if "error" in output:
print("Error:", output["error"])
else:
for result in output:
label = result["label"]
confidence = result["score"]
print("Prediction:", label, ",", confidence, "%")
image = Image.open(image_path)
plt.imshow(image)
plt.axis('off')
plt.show()
def TreeAI_Batch(folder_path, output_csv):
image_paths = []
for filename in os.listdir(folder_path):
if filename.endswith((".jpg", ".jpeg", ".png")):
image_paths.append(os.path.join(folder_path, filename))
num_images = len(image_paths)
results = []
for i, image_path in enumerate(image_paths):
print(f"Processing image {i+1}/{num_images}...")
output = query(image_path)
if "error" in output:
print("Error:", output["error"])
else:
for result in output:
filename = os.path.basename(image_path)
label = result["label"]
confidence = result["score"]
results.append([filename, label, confidence])
with open(output_csv, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(["Filename", "Prediction", "Confidence"])
writer.writerows(results)
|