Spaces:
Sleeping
Sleeping
File size: 2,122 Bytes
f34cb6a |
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 |
# model_1.py
import os
import cv2
import numpy as np
import importlib.util
from PIL import Image
import gradio as gr
from common_detection import perform_detection
MODEL_DIR = 'model'
GRAPH_NAME = 'detect.tflite'
LABELMAP_NAME = 'labelmap.txt'
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
from tflite_runtime.interpreter import Interpreter
else:
from tensorflow.lite.python.interpreter import Interpreter
PATH_TO_CKPT = os.path.join(MODEL_DIR, GRAPH_NAME)
PATH_TO_LABELS = os.path.join(MODEL_DIR, LABELMAP_NAME)
with open(PATH_TO_LABELS, 'r') as f:
labels = [line.strip() for line in f.readlines()]
if labels[0] == '???':
del(labels[0])
interpreter = Interpreter(model_path=PATH_TO_CKPT)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = (input_details[0]['dtype'] == np.float32)
def detect_image(input_image):
image = np.array(input_image)
resized_image = cv2.resize(image, (640, 640))
result_image = perform_detection(resized_image, interpreter, labels, input_details, output_details, height, width, floating_model)
return Image.fromarray(result_image)
def detect_video(input_video):
cap = cv2.VideoCapture(input_video)
frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
resized_frame = cv2.resize(frame, (640, 640))
result_frame = perform_detection(resized_frame, interpreter, labels, input_details, output_details, height, width, floating_model)
frames.append(result_frame)
cap.release()
if not frames:
raise ValueError("No frames were read from the video.")
height, width, layers = frames[0].shape
size = (width, height)
output_video_path = "result_" + os.path.basename(input_video)
out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 15, size)
for frame in frames:
out.write(frame)
out.release()
return output_video_path
|