demo.py

Go to the documentation of this file.

# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
 
import argparse
 
import numpy as np
import cv2 as cv
 
from pphumanseg import PPHumanSeg
 
def str2bool(v):
    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
        return True
    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
        return False
    else:
        raise NotImplementedError
 
backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
try:
    backends += [cv.dnn.DNN_BACKEND_TIMVX]
    targets += [cv.dnn.DNN_TARGET_NPU]
    help_msg_backends += "; {:d}: TIMVX"
    help_msg_targets += "; {:d}: NPU"
except:
    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.')
 
parser = argparse.ArgumentParser(description='PPHumanSeg (https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/contrib/PP-HumanSeg)')
parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='human_segmentation_pphumanseg_2021oct.onnx', help='Path to the model.')
parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
args = parser.parse_args()
 
def get_color_map_list(num_classes):
    """
    Returns the color map for visualizing the segmentation mask,
    which can support arbitrary number of classes.
 
    Args:
        num_classes (int): Number of classes.
 
    Returns:
        (list). The color map.
    """
 
    num_classes += 1
    color_map = num_classes * [0, 0, 0]
    for i in range(0, num_classes):
        j = 0
        lab = i
        while lab:
            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
            j += 1
            lab >>= 3
    color_map = color_map[3:]
    return color_map
 
def visualize(image, result, weight=0.6, fps=None):
    """
    Convert predict result to color image, and save added image.
 
    Args:
        image (str): The input image.
        result (np.ndarray): The predict result of image.
        weight (float): The image weight of visual image, and the result weight is (1 - weight). Default: 0.6
        fps (str): The FPS to be drawn on the input image.
 
    Returns:
        vis_result (np.ndarray): The visualized result.
    """
    color_map = get_color_map_list(256)
    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
    color_map = np.array(color_map).astype(np.uint8)
    # Use OpenCV LUT for color mapping
    c1 = cv.LUT(result, color_map[:, 0])
    c2 = cv.LUT(result, color_map[:, 1])
    c3 = cv.LUT(result, color_map[:, 2])
    pseudo_img = np.dstack((c1, c2, c3))
 
    vis_result = cv.addWeighted(image, weight, pseudo_img, 1 - weight, 0)
 
    if fps is not None:
        cv.putText(vis_result, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
 
    return vis_result
 
 
if __name__ == '__main__':
    # Instantiate PPHumanSeg
    model = PPHumanSeg(modelPath=args.model, backendId=args.backend, targetId=args.target)
 
    if args.input is not None:
        # Read image and resize to 192x192
        image = cv.imread(args.input)
        h, w, _ = image.shape
        image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
        _image = cv.resize(image, dsize=(192, 192))
 
        # Inference
        result = model.infer(_image)
        result = cv.resize(result[0, :, :], dsize=(w, h), interpolation=cv.INTER_NEAREST)
 
        # Draw results on the input image
        image = visualize(image, result)
 
        # Save results if save is true
        if args.save:
            print('Results saved to result.jpg\n')
            cv.imwrite('result.jpg', image)
 
        # Visualize results in a new window
        if args.vis:
            cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
            cv.imshow(args.input, image)
            cv.waitKey(0)
    else: # Omit input to call default camera
        deviceId = 0
        cap = cv.VideoCapture(deviceId)
        w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
        h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
 
        tm = cv.TickMeter()
        while cv.waitKey(1) < 0:
            hasFrame, frame = cap.read()
            if not hasFrame:
                print('No frames grabbed!')
                break
 
            _frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
            _frame = cv.resize(_frame, dsize=(192, 192))
 
            # Inference
            tm.start()
            result = model.infer(_frame)
            tm.stop()
            result = cv.resize(result[0, :, :], dsize=(w, h), interpolation=cv.INTER_NEAREST)
 
            # Draw results on the input image
            frame = visualize(frame, result, fps=tm.getFPS())
 
            # Visualize results in a new window
            cv.imshow('PPHumanSeg Demo', frame)
 
            tm.reset()