import cv2
import numpy as mp
from similarity import *
from hist import *

class PF_Tracker:
    def __init__(self, model, search_space, num_particles=100, state_dims=2,
                 control_std=10, sim_std=20, alpha=0.0):
        self.model = model
        self.search_space = search_space[::-1]
        self.num_particles = num_particles
        self.state_dims = state_dims
        self.control_std = control_std
        self.sim_std = sim_std
        self.alpha = alpha

        #Initialize particles using a uniform distribution
        self.particles = np.array([np.random.uniform(0, self.search_space[i],self.num_particles) for i in range(self.state_dims)]).T
        self.weights = np.ones(len(self.particles)) / len(self.particles)
        self.idxs = np.arange(num_particles)
        self.estimate_state()

    def update(self, frame):
        self.displace()
        self.observe(frame)
        self.resample()
        self.estimate_state()

        if self.alpha > 0:
            self.update_model(frame)

    def displace(self):
        #Displace particles using a normal distribution centered around 0
        self.particles += np.random.normal(0, self.control_std,
                                           self.particles.shape)

    def observe(self, img):
        #Get patches corresponding to each particle
        mh, mw = self.model.shape[:2]
        minx = (self.particles[:,0] - mw/2).astype(np.int)
        miny = (self.particles[:,1] - mh/2).astype(np.int)

        candidates = [img[miny[i]:miny[i]+mh, minx[i]:minx[i]+mw]
                      for i in range(self.num_particles)]

        #Compute importance weight - similarity of each patch to the model
        self.weights = np.array([similarity(cand, self.model, self.sim_std) for cand in candidates])
        self.weights /= np.sum(self.weights)

    def resample(self):
        sw, sh = self.search_space[:2]
        mh, mw = self.model.shape[:2]

        j = np.random.choice(self.idxs, self.num_particles, True,
                             p=self.weights.T) #Sample new particle indices using the distribution of the weights
        
        control = np.random.normal(0, self.control_std, self.particles.shape) #Get a random control input from a normal distribution

        self.particles = np.array(self.particles[j])

        self.particles[:,0] = np.clip(self.particles[:,0], 0, sw - 1)
        self.particles[:,1] = np.clip(self.particles[:,1], 0, sh - 1)

    def estimate_state(self):
        state_idx = np.random.choice(self.idxs, 1, p=self.weights)
        self.state = self.particles[state_idx][0]

    def update_model(self, frame):
        #Get current model based on belief
        mh, mw = self.model.shape[:2]
        minx = int(self.state[0] - mw/2)
        miny = int(self.state[1] - mh/2)
        best_model = frame[miny:miny+mh, minx:minx+mw]

        #Apply appearance model update if new model shape is unchanged
        if best_model.shape == self.model.shape:
            self.model = self.alpha * best_model + (1-self.alpha) * self.model
            self.model = self.model.astype(np.uint8)

    def visualize_filter(self, img):
        self.draw_particles(img)
        self.draw_window(img)
        self.draw_std(img)

    def draw_particles(self, img):
        for p in self.particles:
            cv2.circle(img, tuple(p.astype(int)), 2, (180,255,0), -1)

    def draw_window(self, img):
        best_idx = cv2.minMaxLoc(self.weights)[3][1]
        best_state = self.particles[best_idx]
        pt1 = (best_state - np.array(self.model.shape[::-1])/2).astype(np.int)

        pt2 = pt1 + np.array(self.model.shape[::-1])
        cv2.rectangle(img, tuple(pt1), tuple(pt2), (0,255,0), 2)

    def draw_std(self, img):
        weighted_sum = 0
        dist = np.linalg.norm(self.particles - self.state)
        weighted_sum = np.sum(dist * self.weights.reshape((-1,1)))
        
        cv2.circle(img, tuple(self.state.astype(np.int)),
                   int(weighted_sum), (255,255,255), 1)
import cv2
import numpy as mp
from similarity import *
from hist import *

class PF_Tracker:
    def __init__(self, model, search_space, num_particles=100, state_dims=2,
                 control_std=10, sim_std=20, alpha=0.0):
        self.model = model
        self.search_space = search_space[::-1]
        self.num_particles = num_particles
        self.state_dims = state_dims
        self.control_std = control_std
        self.sim_std = sim_std
        self.alpha = alpha

        #Initialize particles using a uniform distribution
        self.particles = np.array([np.random.uniform(0, self.search_space[i],self.num_particles) for i in range(self.state_dims)]).T
        self.weights = np.ones(len(self.particles)) / len(self.particles)
        self.idxs = np.arange(num_particles)
        self.estimate_state()

    def update(self, frame):
        self.displace()
        self.observe(frame)
        self.resample()
        self.estimate_state()

        if self.alpha > 0:
            self.update_model(frame)

    def displace(self):
        #Displace particles using a normal distribution centered around 0
        self.particles += np.random.normal(0, self.control_std,
                                           self.particles.shape)

    def observe(self, img):
        #Get patches corresponding to each particle
        mh, mw = self.model.shape[:2]
        minx = (self.particles[:,0] - mw/2).astype(np.int)
        miny = (self.particles[:,1] - mh/2).astype(np.int)

        candidates = [img[miny[i]:miny[i]+mh, minx[i]:minx[i]+mw]
                      for i in range(self.num_particles)]

        #Compute importance weight - similarity of each patch to the model
        self.weights = np.array([similarity(cand, self.model, self.sim_std) for cand in candidates])
        self.weights /= np.sum(self.weights)

    def resample(self):
        sw, sh = self.search_space[:2]
        mh, mw = self.model.shape[:2]

        j = np.random.choice(self.idxs, self.num_particles, True,
                             p=self.weights.T) #Sample new particle indices using the distribution of the weights
        
        control = np.random.normal(0, self.control_std, self.particles.shape) #Get a random control input from a normal distribution

        self.particles = np.array(self.particles[j])

        self.particles[:,0] = np.clip(self.particles[:,0], 0, sw - 1)
        self.particles[:,1] = np.clip(self.particles[:,1], 0, sh - 1)

    def estimate_state(self):
        state_idx = np.random.choice(self.idxs, 1, p=self.weights)
        self.state = self.particles[state_idx][0]

    def update_model(self, frame):
        #Get current model based on belief
        mh, mw = self.model.shape[:2]
        minx = int(self.state[0] - mw/2)
        miny = int(self.state[1] - mh/2)
        best_model = frame[miny:miny+mh, minx:minx+mw]

        #Apply appearance model update if new model shape is unchanged
        if best_model.shape == self.model.shape:
            self.model = self.alpha * best_model + (1-self.alpha) * self.model
            self.model = self.model.astype(np.uint8)

    def visualize_filter(self, img):
        self.draw_particles(img)
        self.draw_window(img)
        self.draw_std(img)

    def draw_particles(self, img):
        for p in self.particles:
            cv2.circle(img, tuple(p.astype(int)), 2, (180,255,0), -1)

    def draw_window(self, img):
        best_idx = cv2.minMaxLoc(self.weights)[3][1]
        best_state = self.particles[best_idx]
        pt1 = (best_state - np.array(self.model.shape[::-1])/2).astype(np.int)

        pt2 = pt1 + np.array(self.model.shape[::-1])
        cv2.rectangle(img, tuple(pt1), tuple(pt2), (0,255,0), 2)

    def draw_std(self, img):
        weighted_sum = 0
        dist = np.linalg.norm(self.particles - self.state)
        weighted_sum = np.sum(dist * self.weights.reshape((-1,1)))
        
        cv2.circle(img, tuple(self.state.astype(np.int)),
                   int(weighted_sum), (255,255,255), 1)
#Initialize particles using a uniform distribution #Displace particles using a normal distribution centered around 0 #Get patches corresponding to each particle #Compute importance weight - similarity of each patch to the model #Sample new particle indices using the distribution of the weights #Get a random control input from a normal distribution #Get current model based on belief #Apply appearance model update if new model shape is unchanged