PreProcessor.py

#!/usr/bin/env python
# coding: utf-8

# In[58]:


import matplotlib.pyplot as plt
import pandas as pd
import cv2
import numpy as np
from ultralytics import YOLO 

yolo = YOLO("yolov8n.pt")

# In[59]:


#plt.imshow(cv2.imread("Datasets/images/000003072.jpg"))


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def resize_with_padding(points,image, target_size=(224, 224), padding_color=(0, 0, 0)):
    h, w = image.shape[:2]
    target_w, target_h = target_size

    # Compute the scaling factor
    scale = min(target_w / w, target_h / h)
    new_w, new_h = int(w * scale), int(h * scale)

    # Resize while maintaining aspect ratio
    resized = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)

    # Create a new blank image (padded) with the target size
    padded_image = np.full((target_h, target_w, 3), padding_color, dtype=np.uint8)
    #ajust points
    points = [(int(x * scale + (target_w - new_w) // 2), int(y * scale + (target_h - new_h) // 2)) for x, y in points]
        
    # Compute padding (center the image)
    x_offset = (target_w - new_w) // 2
    y_offset = (target_h - new_h) // 2

    # Place the resized image onto the padded canvas
    padded_image[y_offset:y_offset + new_h, x_offset:x_offset + new_w] = resized
    
    #lambdas to reverse x and y
    reverse = lambda lm,bm,x, y: (int((x - (target_w - new_w) // 2) / scale)+lm, int((y - (target_h - new_h) // 2) / scale)+bm)
    
    return padded_image,points,reverse


# In[61]:


def get_persons(image,points):
    
    results = yolo(image)
    max = 0
    crop = 0,0,0,0
     # Get detected objects
    i = 0
    for result in results:
        for box in result.boxes:
            cls = int(box.cls[0].item())  # Get class ID
            if cls == 0:  # Class '0' is "person" in COCO dataset
                x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())  #
                if i == 0:
                    crop = x1,y1,x2,y2
                    i = 1
                #if this area contains all the points of the person
                sumed = sum([x1 <= x <= x2 and y1 <= y <= y2 for x, y in points])
                if sumed > max:
                    #plt.imshow(cropped_image)
                    max = sumed
                    crop = x1,y1,x2,y2
    return crop


# In[62]:


def transform_data(name,points):
    
    
    if isinstance(name, str):
        image = cv2.imread(path)
        path = "Datasets/images/"+name
        if len(points) == 0:
            path = name
    else:
        image = name
    leftmost,bottommost,rightmost,topmost = get_persons(image,points)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    # Ensure the coordinates are within the image bounds
    leftmost = max(leftmost, 0)
    bottommost = max(bottommost, 0)
    rightmost = min(rightmost, image.shape[1])
    topmost = min(topmost, image.shape[0])

    # Cut image from the points
    image = image[bottommost:topmost, leftmost:rightmost]

    # Adjust points coordinates
    points = [(x - leftmost, y - bottommost) for x, y in points]
    padded_image,new_points,reverse = resize_with_padding(points,image)
    reverse_complete = lambda x, y: reverse(leftmost, bottommost,x, y )
    return padded_image,new_points,reverse_complete
    # Plot image
    


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# In[63]:



#df = pd.read_csv('./Datasets/mpii_human_pose.csv')
#df = df[df["NAME"]=="000003072.jpg"]
# Load image using OpenCV (convert BGR to RGB for Matplotlib)

keypoints = [
    ("r ankle_X", "r ankle_Y"),
    ("r knee_X", "r knee_Y"),
    ("r hip_X", "r hip_Y"),
    ("l hip_X", "l hip_Y"),
    ("l knee_X", "l knee_Y"),
    ("l ankle_X", "l ankle_Y"),
    ("pelvis_X", "pelvis_Y"),
    ("thorax_X", "thorax_Y"),
    ("upper neck_X", "upper neck_Y"),
    ("head top_X", "head top_Y"),
    ("r wrist_X", "r wrist_Y"),
    ("r elbow_X", "r elbow_Y"),
    ("r shoulder_X", "r shoulder_Y"),
    ("l shoulder_X", "l shoulder_Y"),
    ("l elbow_X", "l elbow_Y"),
    ("l wrist_X", "l wrist_Y")
]

## Select the first row (example: first image)
#row = df.iloc[0]  # Change index for other images
#
## Convert keypoints into a list of (x, y) tuples
#points = [(int(row[x]), int(row[y])) for x, y in keypoints]
#image,points,reverse = transform_data("000003072.jpg",points)
## Plot image
#plt.imshow(image)
#for (x, y) in points:
#    plt.scatter(x, y, color="red", s=30)  # Red points
#
#plt.show()


# In[64]:


#original_points = [reverse(x,y) for x, y in points]
#plt.imshow(cv2.imread("Datasets/images/000003072.jpg"))
#for (x, y) in original_points:
#    plt.scatter(x, y, color="red", s=30)  # Red points
#
#plt.show()


# In[65]:


# change the datasenumpy.core._exceptions._UFuncNoLoopError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U16'), dtype('uint8')) -> None
#t using a function that will return the image with the alterations and the new points
def process_row(row):
    points = [(int(row[x]), int(row[y])) for x, y in keypoints]
    try:
        image, points,_ = transform_data(row["NAME"], points)
    except Exception as e:
        print(f"Error processing row {row['ID']}: {e}")
        row["image"] = None
        return row
    row["r ankle_X"], row["r ankle_Y"] = points[0]
    row["r knee_X"], row["r knee_Y"] = points[1]
    row["r hip_X"], row["r hip_Y"] = points[2]
    row["l hip_X"], row["l hip_Y"] = points[3]
    row["l knee_X"], row["l knee_Y"] = points[4]
    row["l ankle_X"], row["l ankle_Y"] = points[5]
    row["pelvis_X"], row["pelvis_Y"] = points[6]
    row["thorax_X"], row["thorax_Y"] = points[7]
    row["upper neck_X"], row["upper neck_Y"] = points[8]
    row["head top_X"], row["head top_Y"] = points[9]
    row["r wrist_X"], row["r wrist_Y"] = points[10]
    row["r elbow_X"], row["r elbow_Y"] = points[11]
    row["r shoulder_X"], row["r shoulder_Y"] = points[12]
    row["l shoulder_X"], row["l shoulder_Y"] = points[13]
    row["l elbow_X"], row["l elbow_Y"] = points[14]
    row["l wrist_X"], row["l wrist_Y"] = points[15]
    row["image"] = image
    
    return row


# In[66]:


def process_dataset(name,df,numberRows):
    df = pd.read_csv(name)
    df= df[(df != -1).all(axis=1)]
    df = df[:numberRows].apply(process_row, axis=1)
    #takes a long TIME !! for me 1h 30 min

    df.to_pickle('dataset'+str(df.shape[0])+'.pkl')
    
    return df
    
    


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#newDF = process_dataset("./Datasets/mpii_human_pose.csv")


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#row = newDF.iloc[5]  # Change index for other images
## Convert keypoints into a list of (x, y) tuples
#points = [(int(row[x]), int(row[y])) for x, y in keypoints]
#plt.imshow(row["image"])
#for (x, y) in points:
#    plt.scatter(x, y, color="red", s=30)  # Red points


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#get all rows that have image null
#df_nulls = newDF[newDF["image"].isnull()]
## count how mutch image nulls it has
#print(df_nulls.shape)
#row = df_nulls.iloc[0]  # Change index for other images
#points = [(int(row[x]), int(row[y])) for x, y in keypoints]
#print(get_persons(cv2.imread("./Datasets/images/"+row["NAME"]),points))
#plt.imshow(cv2.imread("./Datasets/images/"+row["NAME"]))
#for (x, y) in points:
#    plt.scatter(x, y, color="red", s=30)


# In[47]:


#df5000 = pd.read_pickle('dataset5000.pkl')
#df6231 = pd.read_pickle('dataset6231.pkl')
#df = pd.concat([df5000, df6231], ignore_index=True)
#df.to_pickle('dataset11231.pkl')