Anxiety_Detection/pose_model.py at master · bhawargi/Anxiety_Detection · GitHub

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import cv2
import matplotlib
import pylab as plt
import numpy as np
import math
import tensorflow as tf

from numpy import ma
from tensorflow.keras.models import load_model
from models.mobilenet_model import get_mobilenet_model
from scipy.ndimage.filters import gaussian_filter
######################################################
weights_path = "weights.best.mobilenet.h5"
model = get_mobilenet_model(1.0, 224)
model.load_weights(weights_path)
print("model loaded OK")
#########################################
def get_points(oriImg):
    canvas = cv2.resize(oriImg,(224,224))
    oriImg = cv2.resize(oriImg,(224,224))
    input_img = np.transpose(np.float32(oriImg[:,:,:,np.newaxis]), (3,0,1,2))
    input_img = oriImg[np.newaxis,...]
    print("Input shape: " + str(input_img.shape))
    output_blobs = model.predict(input_img)
    paf = output_blobs[2]
    heatmap = output_blobs[3]
    heatmap = heatmap[0]
    paf = paf[0]
    U = paf[:,:,16] * -1
    V = paf[:,:,17]
    U = cv2.resize(U, (0,0), fx=8, fy=8, interpolation=cv2.INTER_CUBIC)
    V = cv2.resize(V, (0,0), fx=8, fy=8, interpolation=cv2.INTER_CUBIC)


    X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))
    M = np.zeros(U.shape, dtype='bool')
    M[U**2 + V**2 < 0.5 * 0.5] = True
    U = ma.masked_array(U, mask=M)
    V = ma.masked_array(V, mask=M)
    all_peaks = []
    peak_counter = 0
    thre1 = 0.1

    for part in range(18):
        map = heatmap[:,:,part]
        #map = gaussian_filter(map_ori, sigma=3)

        map_left = np.zeros(map.shape)
        map_left[1:,:] = map[:-1,:]
        map_right = np.zeros(map.shape)
        map_right[:-1,:] = map[1:,:]
        map_up = np.zeros(map.shape)
        map_up[:,1:] = map[:,:-1]
        map_down = np.zeros(map.shape)
        map_down[:,:-1] = map[:,1:]

        peaks_binary = np.logical_and.reduce((map>=map_left, map>=map_right, map>=map_up, map>=map_down, map > thre1))
        peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse
        peaks_with_score = [x + (map[x[1],x[0]],) for x in peaks]
        id = range(peak_counter, peak_counter + len(peaks))
        peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]

        all_peaks.append(peaks_with_score_and_id)
        peak_counter += len(peaks)
    # find connection in the specified sequence, center 29 is in the position 15
    limbSeq = [[2,3], [2,6], [3,4], [4,5], [6,7], [7,8], [2,9], [9,10], \
               [10,11], [2,12], [12,13], [13,14], [2,1], [1,15], [15,17], \
               [1,16], [16,18], [3,17], [6,18]]
    # the middle joints heatmap correpondence
    mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], \
              [23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], \
              [55,56], [37,38], [45,46]]

    thre2 = 0.05

    connection_all = []
    special_k = []
    mid_num = 10

    for k in range(len(mapIdx)):
        score_mid = paf[:,:,[x-19 for x in mapIdx[k]]]
        candA = all_peaks[limbSeq[k][0]-1]
        candB = all_peaks[limbSeq[k][1]-1]
        nA = len(candA)
        nB = len(candB)
        indexA, indexB = limbSeq[k]
        if(nA != 0 and nB != 0):
            connection_candidate = []
            for i in range(nA):
                for j in range(nB):
                    vec = np.subtract(candB[j][:2], candA[i][:2])
                    norm = math.sqrt(vec[0]*vec[0] + vec[1]*vec[1])
                    # failure case when 2 body parts overlaps
                    if norm == 0:
                        continue
                    vec = np.divide(vec, norm)

                    startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
                                   np.linspace(candA[i][1], candB[j][1], num=mid_num)))

                    vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
                                      for I in range(len(startend))])
                    vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
                                      for I in range(len(startend))])

                    score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
                    score_with_dist_prior = sum(score_midpts)/len(score_midpts) + min(0.5*oriImg.shape[0]/norm-1, 0)
                    criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
                    criterion2 = score_with_dist_prior > 0
                    if criterion1 and criterion2:
                        connection_candidate.append([i, j, score_with_dist_prior, score_with_dist_prior+candA[i][2]+candB[j][2]])

            connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
            connection = np.zeros((0,5))
            for c in range(len(connection_candidate)):
                i,j,s = connection_candidate[c][0:3]
                if(i not in connection[:,3] and j not in connection[:,4]):
                    connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
                    if(len(connection) >= min(nA, nB)):
                        break

            connection_all.append(connection)
        else:
            special_k.append(k)
            connection_all.append([])

    # last number in each row is the total parts number of that person
    # the second last number in each row is the score of the overall configuration
    subset = -1 * np.ones((0, 20))
    candidate = np.array([item for sublist in all_peaks for item in sublist])

    for k in range(len(mapIdx)):
        if k not in special_k:
            partAs = connection_all[k][:,0]
            partBs = connection_all[k][:,1]
            indexA, indexB = np.array(limbSeq[k]) - 1

            for i in range(len(connection_all[k])): #= 1:size(temp,1)
                found = 0
                subset_idx = [-1, -1]
                for j in range(len(subset)): #1:size(subset,1):
                    if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
                        subset_idx[found] = j
                        found += 1

                if found == 1:
                    j = subset_idx[0]
                    if(subset[j][indexB] != partBs[i]):
                        subset[j][indexB] = partBs[i]
                        subset[j][-1] += 1
                        subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
                elif found == 2: # if found 2 and disjoint, merge them
                    j1, j2 = subset_idx
                    print ("found = 2")
                    membership = ((subset[j1]>=0).astype(int) + (subset[j2]>=0).astype(int))[:-2]
                    if len(np.nonzero(membership == 2)[0]) == 0: #merge
                        subset[j1][:-2] += (subset[j2][:-2] + 1)
                        subset[j1][-2:] += subset[j2][-2:]
                        subset[j1][-2] += connection_all[k][i][2]
                        subset = np.delete(subset, j2, 0)
                    else: # as like found == 1
                        subset[j1][indexB] = partBs[i]
                        subset[j1][-1] += 1
                        subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]

                # if find no partA in the subset, create a new subset
                elif not found and k < 17:
                    row = -1 * np.ones(20)
                    row[indexA] = partAs[i]
                    row[indexB] = partBs[i]
                    row[-1] = 2
                    row[-2] = sum(candidate[connection_all[k][i,:2].astype(int), 2]) + connection_all[k][i][2]
                    subset = np.vstack([subset, row])


    # delete some rows of subset which has few parts occur
    deleteIdx = [];
    for i in range(len(subset)):
        if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4:
            deleteIdx.append(i)
    subset = np.delete(subset, deleteIdx, axis=0)


    scale = 8

    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
    cmap = matplotlib.cm.get_cmap('hsv')
    req = {}
    keypt = []
    for i in range(2,8):
        rgba = np.array(cmap(1 - i/18. - 1./36))
        rgba[0:3] *= 255
        for j in range(len(all_peaks[i])):
            x = all_peaks[i][j][0] * scale
            y = all_peaks[i][j][1] * scale
            keypt.append((x,y))
    return keypt
    # plt.imshow(oriImg)
    # for i in keypt:
    #     cv2.circle(oriImg, i, 4, (0,255,0), thickness=-1)
    # plt.imshow(oriImg)