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import os
os.environ['TF_USE_LEGACY_KERAS']='1'
import gradio as gr
import tensorflow as tf
import numpy as np
import requests
import torch
from huggingface_hub import snapshot_download
from huggingface_hub import hf_hub_download
show_all = False
label_count = 51
def readLines(filename):
with open(filename,'r') as f:
return(f.read().splitlines())
labels = ["missing"] * label_count
labels = readLines('labels.txt')
valid = readLines('valid.txt')
model_path = snapshot_download(repo_id="eshieh2/jaguarid_pantanal")
model = tf.saved_model.load(f"{model_path}/saved_model")
serving = model.signatures['serving_default']
detector_path = hf_hub_download(repo_id= "eshieh2/jaguarhead",
filename = "jaguarheadv5.pt")
detector = torch.hub.load('ultralytics/yolov5', 'custom', path = detector_path)
def classify_image(in_image):
width,height = in_image.size
heads = detector(in_image)
masks = [] # tuple of box coords and string
for head in heads.xyxy[0]:
x,y,x2,y2,pct,cls = head.numpy()
w = x2 - x
h = y2 - y
inp = in_image.crop((x,y,x2,y2))
inp = inp.resize((480,480))
inp = np.array(inp)
inp = np.reshape(inp,(-1, 480, 480, 3)).astype(np.float32)
inp = np.divide(inp,255.0)
prediction = serving(tf.convert_to_tensor(inp))['model']
prediction = tf.squeeze(prediction)
pred = {labels[i]: float(prediction[i]) for i in range(label_count)}
#print(pred)
max_key = max(pred, key=pred.get)
rect = (int(x),int(y),int(x2),int(y2))
if show_all or max_key.lower() in valid:
masks.append((rect,f"{max_key}:{pct}"))
else:
masks.append((rect,f"unknown",))
return (in_image,masks)
image = gr.Image(type='pil')
output = gr.AnnotatedImage()
gr.Interface(fn=classify_image, inputs=image, outputs=output, examples = [["kasimir.jpg"],["guaraci.jpg"],["marcela.jpg"]]).launch()
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