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| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| def main(): | |
| st.set_page_config(page_title="Unmasked the Target Customers", page_icon="🦜") | |
| st.header("Turn the photos taken in the campaign to useful marketing insights") | |
| uploaded_file = st.file_uploader("Select an Image...") | |
| # define a function to extract the sub-image using | |
| def extract_subimage(image, xmin, xmax, ymin, ymax): | |
| # crop the sub-image using the provided coordinates | |
| sub_image = image.crop((xmin, ymin, xmax, ymax)) | |
| # return the extracted sub-image | |
| return sub_image | |
| def pipeline_1_final(image_lst): | |
| pipe = pipeline("object-detection", model="hustvl/yolos-tiny") | |
| preds = pipe(image) | |
| person_count = 0 | |
| sub_image_lst = [] | |
| for pred in preds: | |
| if pred['label'] == 'person': | |
| person_count +=1 | |
| box = pred['box'] | |
| xmin, ymin, xmax, ymax = box.values() | |
| sub_image = extract_subimage(image,xmin, xmax, ymin, ymax) | |
| sub_image_lst += [sub_image] | |
| return sub_image_lst, person_count | |
| def pipeline_2_final(image_lst): | |
| age_lst = [] | |
| age_mapping = {"0-2": "lower than 10", | |
| "3-9": "lower than 10", | |
| "10-19":"10-19", | |
| "20-29":"20-29", | |
| "30-39":"30-39", | |
| "40-49":"40-49", | |
| "50-59":"50-59", | |
| "60-69":"60-69", | |
| "more than 70" : "70 or above"} | |
| pipe = pipeline("image-classification", model="nateraw/vit-age-classifier") | |
| for image in image_lst: | |
| preds = pipe(image) | |
| preds_age_range = preds[0]['label'] | |
| preds_age_range = age_mapping[preds_age_range] | |
| age_lst +=[preds_age_range] | |
| return age_lst | |
| def pipeline_3_final(image_lst): | |
| gender_lst = [] | |
| pipe = pipeline("image-classification", model="mikecho/NTQAI_pedestrian_gender_recognition_v1") | |
| for image in image_lst: | |
| preds = pipe(image) | |
| preds_gender = preds[0]['label'] | |
| gender_lst +=[preds_gender] | |
| return gender_lst | |
| def gender_prediciton_model_NTQAI_pedestrian_gender_recognition(image_lst): | |
| gender_lst = [] | |
| pipe = pipeline("image-classification", model="NTQAI/pedestrian_gender_recognition") | |
| for image in image_lst: | |
| preds = pipe(image) | |
| preds_gender = preds[0]['label'] | |
| gender_lst +=[preds_gender] | |
| return gender_lst | |
| def pipeline_4_final(image_lst): | |
| start_time = time.time() | |
| pipe = pipeline("image-classification", model="dima806/facial_emotions_image_detection") | |
| preds_lst = [] | |
| for image in image_lst: | |
| preds = pipe(image) | |
| preds_emotion = preds[0]['label'] | |
| preds_lst +=[preds_emotion] | |
| return preds_lst | |
| def generate_gender_tables(gender_list, age_list, emotion_list): | |
| gender_count = {} | |
| for gender, age, emotion in zip(gender_list, age_list, emotion_list): | |
| if age not in gender_count: | |
| gender_count[age] = {'male': 0, 'female': 0} | |
| gender_count[age][gender] += 1 | |
| happiness_percentage = {} | |
| for gender, age, emotion in zip(gender_list, age_list, emotion_list): | |
| if age not in happiness_percentage: | |
| happiness_percentage[age] = {'male': 0, 'female': 0} | |
| if emotion == 'happiness': | |
| happiness_percentage[age][gender] += 1 | |
| table1 = [] | |
| for age, count in gender_count.items(): | |
| male_count = count['male'] | |
| female_count = count['female'] | |
| table1.append([age, male_count, female_count]) | |
| table2 = [] | |
| for age, happiness in happiness_percentage.items(): | |
| male_count = gender_count[age]['male'] | |
| female_count = gender_count[age]['female'] | |
| male_percentage = (happiness['male'] / male_count) * 100 if male_count > 0 else 0 | |
| female_percentage = (happiness['female'] / female_count) * 100 if female_count > 0 else 0 | |
| table2.append([age, male_percentage, female_percentage]) | |
| return table1, table2 | |
| if uploaded_file is not None: | |
| print(uploaded_file) | |
| image = Image.open(uploaded_file) | |
| st.image(uploaded_file, caption="Processing Image", use_column_width=True) | |
| pipeline_1_out, person_count = pipeline_1_final(image) | |
| pipeline_2_age = pipeline_2_final(pipeline_1_out) | |
| pipeline_3_gender = pipeline_3_final(pipeline_1_out) | |
| pipeline_4_emotion = pipeline_3_final(pipeline_1_out) | |
| table1, table2 = generate_gender_tables(pipeline_3_gender, pipeline_2_age, pipeline_4_emotion) | |
| st.text('The detected number of person:', person_count) | |
| st.text('\nGender and Age Group Distribution') | |
| st.text('Age, Male, Female') | |
| for row in table1: | |
| print(row) | |
| st.text('\nShare of Happniess') | |
| st.text('Age, Male, Female') | |
| for row in table2: | |
| print(row) | |
| if __name__ == "__main__": | |
| main() |