app.py

import cv2
import numpy as np
import tensorflow as tf
#from tensorflow.compat.v1 import ConfigProto
from tensorflow.python.saved_model import tag_constants
import gradio as gr

from utils import crop_objects, format_boxes, CLASS_NAMES

def main(image, 
         input_size:int=416,
         weights: str="yolov4-416",
         iou:float=0.45,
         score: float=0.50):
    #config = ConfigProto()
    #config.gpu_options.allow_growth = True

    # load model
    saved_model_loaded = tf.saved_model.load(weights, tags=[tag_constants.SERVING])

    # loop through images in list and run Yolov4 model on each
    # original_image = cv2.imread(image)
    original_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    image_data = cv2.resize(original_image, (input_size, input_size))
    image_data = image_data / 255.

    images_data = np.asarray(image_data).astype(np.float32)

    print('l1',images_data.shape)

    infer = saved_model_loaded.signatures['serving_default']
    batch_data = tf.constant(images_data)
    pred_bbox = infer(batch_data)
    for key, value in pred_bbox.items():
        boxes = value[:, :, 0:4]
        pred_conf = value[:, :, 4:]
    print('l2',pred_conf.shape)
    print('l22',boxes.shape)

    # run non max suppression on detections
    boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
        boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
        scores=tf.reshape(
            pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
        max_output_size_per_class=50,
        max_total_size=50,
        iou_threshold=iou,
        score_threshold=score
    )

    print("Shape of boxes:", boxes.shape)
    print("Shape of pred_conf:", pred_conf.shape)


    # format bounding boxes from normalized ymin, xmin, ymax, xmax ---> xmin, ymin, xmax, ymax
    original_h, original_w, _ = original_image.shape
    bboxes = format_boxes(boxes.numpy()[0], original_h, original_w)
        
        # hold all detection data in one variable
    pred_bbox = [bboxes, scores.numpy()[0], classes.numpy()[0], valid_detections.numpy()[0]]


    allowed_classes = CLASS_NAMES

    crop_img = crop_objects(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB), pred_bbox, allowed_classes)
    return crop_img

demo = gr.Interface(fn=main, inputs="image", outputs="image")
demo.launch()