mshack/finalmost.py at master · prachijaseja/mshack · GitHub

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# coding: utf-8

# In[1]:
import time


import os
import sys
import numpy as np
import cv2
#sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread, imsave
from skimage.measure import structural_similarity as ssim
from config import load_config
from dataset.factory import create as create_dataset
from nnet import predict
from util import visualize
import cv2
from dataset.pose_dataset import data_to_input


from multiperson.detections import extract_detections
from multiperson.predict import SpatialModel, eval_graph, get_person_conf_multicut
from multiperson.visualize import PersonDraw, visualize_detections
import matplotlib.pyplot as plt
import tensorflow as tf


# In[10]:


def mse(imageA,imageB):
    err = np.sum((imageA.astype("float")-imageB.astype("float"))**2)
    err /= float(imageA.shape[0]*imageA.shape[1])
    return err

def compare_images(imageA, imageB, title):
    m = mse(imageA, imageB)
    s = ssim(imageA, imageB)
    fig = plt.figure(title)
    plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s))

    ax = fig.add_subplot(1, 2, 1)
    plt.imshow(imageA, cmap = plt.cm.gray)
    plt.axis("off")
    ax = fig.add_subplot(1, 2, 2)

    plt.imshow(imageB, cmap = plt.cm.gray)
    plt.axis("off")
    plt.show()

    return(s,m)


# In[6]:

def main():
    start_time=time.time()
    print("main hai")
    tf.reset_default_graph()
    cfg = load_config("demo/pose_cfg_multi.yaml")
    dataset = create_dataset(cfg)
    sm = SpatialModel(cfg)
    sm.load()
    draw_multi = PersonDraw()
    # Load and setup CNN part detector
    sess, inputs, outputs = predict.setup_pose_prediction(cfg)

    # Read image from file
    dir=os.listdir("stick")
    k=0
    cap=cv2.VideoCapture(0)
    i=0
    while (cap.isOpened()):
            if i%20 == 0:
                ret, orig_frame= cap.read()
                if ret==True:
                    frame = cv2.resize(orig_frame, (0, 0), fx=0.30, fy=0.30)
                    image= frame
                    sse=0
                    mse=0

                    image_batch = data_to_input(frame)

                    # Compute prediction with the CNN
                    outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})

                    scmap, locref, pairwise_diff = predict.extract_cnn_output(outputs_np, cfg, dataset.pairwise_stats)

                    detections = extract_detections(cfg, scmap, locref, pairwise_diff)

                    unLab, pos_array, unary_array, pwidx_array, pw_array = eval_graph(sm, detections)

                    person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
                    img = np.copy(image)
                    #coor = PersonDraw.draw()
                    visim_multi = img.copy()
                    co1=draw_multi.draw(visim_multi, dataset, person_conf_multi)
                    plt.imshow(visim_multi)
                    plt.show()
                    visualize.waitforbuttonpress()
                    #print("this is draw : ", co1)
                    if k==1:
                        qwr = np.zeros((1920,1080,3), np.uint8)

                        cv2.line(qwr, co1[5][0], co1[5][1],(255,0,0),3)
                        cv2.line(qwr, co1[7][0], co1[7][1],(255,0,0),3)
                        cv2.line(qwr, co1[6][0], co1[6][1],(255,0,0),3)
                        cv2.line(qwr, co1[4][0], co1[4][1],(255,0,0),3)

                        cv2.line(qwr, co1[9][0], co1[9][1],(255,0,0),3)
                        cv2.line(qwr, co1[11][0], co1[11][1],(255,0,0),3)
                        cv2.line(qwr, co1[8][0], co1[8][1],(255,0,0),3)
                        cv2.line(qwr, co1[10][0], co1[10][1],(255,0,0),3)
                        # In[9]:
                        cv2.imshow('r',qwr)
                        qwr2="stick/frame"+str(k)+".jpg"
                        qw1 = cv2.cvtColor(qwr, cv2.COLOR_BGR2GRAY)
                        qw2= cv2.cvtColor(qwr2, cv2.COLOR_BGR2GRAY)

                        fig = plt.figure("Images")
                        images = ("Original", qw1), ("Contrast", qw2)
                        for (i, (name, image)) in enumerate(images):
                                ax = fig.add_subplot(1, 3, i + 1)
                                ax.set_title(name)
                        plt.imshow(hash(tuple(image)))
                        # compare the images
                        s,m=compare_images(qw1, qw2, "Image1 vs Image2")
                        k+=1
                        sse=s
                        mse=m

                else:
                    break
    elapsed= time.time()-start_time
    #print("sse score : ", sse)
    print("Mean squared error : ", elapsed/100)
    cap.release()
    cv2.destroyAllWindows()

if  __name__=='__main__':
    main()